In industrial manufacturing,efficient surface defect detection is crucial for ensuring product quality and production safety.Traditional inspectionmethods are often slow,subjective,and prone to errors,while classicalm...In industrial manufacturing,efficient surface defect detection is crucial for ensuring product quality and production safety.Traditional inspectionmethods are often slow,subjective,and prone to errors,while classicalmachine vision techniques strugglewith complex backgrounds and small defects.To address these challenges,this study proposes an improved YOLOv11 model for detecting defects on hot-rolled steel strips using the NEU-DET dataset.Three key improvements are introduced in the proposed model.First,a lightweight Guided Attention Feature Module(GAFM)is incorporated to enhance multi-scale feature fusion,allowing the model to better capture and integrate semantic and spatial information across different layers,which improves its ability to detect defects of varying sizes.Second,an Aggregated Attention(AA)mechanism is employed to strengthen the representation of critical defect features while effectively suppressing irrelevant background information,particularly enhancing the detection of small,low-contrast,or complex defects.Third,Ghost Dynamic Convolution(GDC)is applied to reduce computational cost by generating low-cost ghost features and dynamically reweighting convolutional kernels,enabling faster inference without sacrificing feature quality or detection accuracy.Extensive experiments demonstrate that the proposed model achieves a mean Average Precision(mAP)of 87.2%,compared to 81.5%for the baseline,while lowering computational cost from6.3Giga Floating-point Operations Per Second(GFLOPs)to 5.1 GFLOPs.These results indicate that the improved YOLOv11 is both accurate and computationally efficient,making it suitable for real-time industrial surface defect detection and contributing to the development of practical,high-performance inspection systems.展开更多
A novel dual-branch decoding fusion convolutional neural network model(DDFNet)specifically designed for real-time salient object detection(SOD)on steel surfaces is proposed.DDFNet is based on a standard encoder–decod...A novel dual-branch decoding fusion convolutional neural network model(DDFNet)specifically designed for real-time salient object detection(SOD)on steel surfaces is proposed.DDFNet is based on a standard encoder–decoder architecture.DDFNet integrates three key innovations:first,we introduce a novel,lightweight multi-scale progressive aggregation residual network that effectively suppresses background interference and refines defect details,enabling efficient salient feature extraction.Then,we propose an innovative dual-branch decoding fusion structure,comprising the refined defect representation branch and the enhanced defect representation branch,which enhance accuracy in defect region identification and feature representation.Additionally,to further improve the detection of small and complex defects,we incorporate a multi-scale attention fusion module.Experimental results on the public ESDIs-SOD dataset show that DDFNet,with only 3.69 million parameters,achieves detection performance comparable to current state-of-the-art models,demonstrating its potential for real-time industrial applications.Furthermore,our DDFNet-L variant consistently outperforms leading methods in detection performance.The code is available at https://github.com/13140W/DDFNet.展开更多
The field spectroradiometer was used to measure spectra of different snow and snow-covered land surface objects in Beijing area.The result showed that for a pure snow spectrum,the snow reflectance peaks appeared from ...The field spectroradiometer was used to measure spectra of different snow and snow-covered land surface objects in Beijing area.The result showed that for a pure snow spectrum,the snow reflectance peaks appeared from visible to 800 nm band locations;there was an obvious absorption valley of snow spectrum near 1 030 nm wavelength.Compared with fresh snow,the reflection peaks of the old snow and melting snow showed different degrees of decline in the ranges of 300～1 300,1 700～1 800 and 2 200～2 300 nm,the lowest was from the compacted snow and frozen ice.For the vegetation and snow mixed spectral characteristics,it was indicated that the spectral reflectance increased for the snow-covered land types(including pine leaf with snow and pine leaf on snow background), due to the influence of snow background in the range of 350～1 300 nm.However, the spectrum reflectance of mixed pixel remained a vegetation spectral characteristic.In the end,based on the spectrum analysis of snow,vegetation,and mixed snow/vegetation pixels,the mixed spectral fitting equations were established,and the results showed that there was good correlation between spectral curves by simulation fitting and observed ones(correlation coefficient R2=0.950 9).展开更多
Efficient detection of surface defects is primary for ensuring product quality during manufacturing processes.To enhance the performance of deep learning-based methods in practical applications,the authors propose Den...Efficient detection of surface defects is primary for ensuring product quality during manufacturing processes.To enhance the performance of deep learning-based methods in practical applications,the authors propose Dense-YOLO,a fast surface defect detection network that combines the strengths of DenseNet and you only look once version 3(YOLOv3).The authors design a lightweight backbone network with improved densely connected blocks,optimising the utilisation of shallow features while maintaining high detection speeds.Additionally,the authors refine the feature pyramid network of YOLOv3 to increase the recall of tiny defects and overall positioning accuracy.Furthermore,an online multi-angle template matching technique is introduced based on normalised cross-correlation to precisely locate the detection area.This refined template matching method not only accelerates detection speed but also mitigates the influence of the background.To validate the effectiveness of our enhancements,the authors conduct comparative experiments across two private datasets and one public dataset.Results show that Dense-YOLO outperforms existing methods,such as faster R-CNN,YOLOv3,YOLOv5s,YOLOv7,and SSD,in terms of mean average precision(mAP)and detection speed.Moreover,Dense-YOLO outperforms networks inherited from VGG and ResNet,including improved faster R-CNN,FCOS,M2Det-320 and FRCN,in mAP.展开更多
Exploiting remote sensing data is a promising approach to estimate surface solar irradiance(SSI).In this study,we propose a method to estimate global SSI using a lookup table and Landsat data.Despite the low temporal ...Exploiting remote sensing data is a promising approach to estimate surface solar irradiance(SSI).In this study,we propose a method to estimate global SSI using a lookup table and Landsat data.Despite the low temporal resolution of the data used,the developed method produces SSI maps with adequate spatial resolution.It combines physical parameters extracted from Landsat metadata files with the physical laws governing global solar irradiance,its transmission through the atmosphere,and surface reflectance.The results obtained are compared with those in the literature,particularly one study that uses Meteosat data and two others that use radiometric spectral and temporal models.Additionally,experiments are conducted at three sites in Algeria:Oran,In Amenas,and Tamenghasset.The findings indicate that the proposed approach aligns with the tested literature methods while providing SSI maps with superior spatial resolution.Furthermore,the obtained solar irradiances exhibit a root mean square error of approximately 190 W m^(−2)μm^(−1) compared with those of the Bird and Riordan spectral model,and approximately 50 W m^(−2) compared with the results from the Bird and Hulstrom temporal model,and are also comparable to the results of previous studies.展开更多
In response to themissed and false detections that are easily caused by the large variety of and significant differences among aluminum surface defects,a detection algorithm based on an improved You Only Look Once(YOL...In response to themissed and false detections that are easily caused by the large variety of and significant differences among aluminum surface defects,a detection algorithm based on an improved You Only Look Once(YOLO)v8n network is proposed.First,a C2f_DWR_DRB module is constructed by introducing a dilation-wise residual(DWR)module and a dilated reparameterization block(DRB)to replace the C2f module at the high level of the backbone network,enriching the gradient flow information and increasing the effective receptive field(ERF).Second,an efficient local attention(ELA)mechanism is fused with the high-level screening-feature pyramid networks(HS-FPN)module,and an ELA_HSFPN is designed to replace the original feature fusion module,enhancing the ability of the network to cope with multiscale detection tasks.Moreover,a lightweight shared convolutional detection head(SCDH)is introduced to reduce the number of parameters and the computational complexity of the module while enhancing the performance and generalizability of the model.Finally,the soft intersection over union(SIoU)replaces the original loss function to improve the convergence speed and prediction accuracy of the model.Experimental results show that compared with that of the original YOLOv8n model,the mAP@0.5 of the improved algorithm is increased by 5.1%,the number of parameters and computational complexity are reduced by 33.3%and 32.1%,respectively,and the FPS is increased by 4.9%.Compared with other mainstream object detection algorithms,the improved algorithm still leads in terms of core indicators and has good generalizability for surface defects encountered in other industrial scenarios.展开更多
The optimum friction welding （FW） parameters of duplex stainless steel （DSS） UNS $32205 joint was determined. The experiment was carried out as the central composite array of 30 experiments. The selected input par...The optimum friction welding （FW） parameters of duplex stainless steel （DSS） UNS $32205 joint was determined. The experiment was carried out as the central composite array of 30 experiments. The selected input parameters were friction pressure （F）, upset pressure （U）, speed （S） and burn-off length （B）, and responses were hardness and ultimate tensile strength. To achieve the quality of the welded joint, the ultimate tensile strength and hardness were maximized, and response surface methodology （RSM） was applied to create separate regression equations of tensile strength and hardness. Intelligent optimization technique such as genetic algorithm was used to predict the Pareto optimal solutions. Depending upon the application, preferred suitable welding parameters were selected. It was inferred that the changing hardness and tensile strength of the friction welded joint influenced the upset pressure, friction Pressure and speed of rotation.展开更多
Since the volume transport across the pycnocline is much smaller than that in the mixed layer, the current in the mixed layer can be regarded as non-divergent. An objective analysis method is deduced based on this hyp...Since the volume transport across the pycnocline is much smaller than that in the mixed layer, the current in the mixed layer can be regarded as non-divergent. An objective analysis method is deduced based on this hypothesis. The linear combination method is used to solve the non-divergent component of the current field of an ocean basin containing islands,which is equivalent to a mathematical problem of solving a Poisson equation in a multi-connected domain. The method is applied to the Bohai Sea, the Yellow Sea and the East China Sea (ECS). The modeled result is consistent with the current maps constructed by other oceanographers.展开更多
Surface defects can affect the quality of steel plate.Many methods based on computer vision are currently applied to surface defect detection of steel plate.However,their real-time performance and object detection of ...Surface defects can affect the quality of steel plate.Many methods based on computer vision are currently applied to surface defect detection of steel plate.However,their real-time performance and object detection of small defect are still unsatisfactory.An improved object detection network based on You Only Look One-level Feature(YOLOF)is proposed to show excellent performance in surface defect detection of steel plate,called DLF-YOLOF.First,the anchor-free detector is used to reduce the network hyperparameters.Secondly,deformable convolution network and local spatial attention module are introduced into the feature extraction network to increase the contextual information in the feature maps.Also,the soft non-maximum suppression is used to improve detection accuracy significantly.Finally,data augmentation is performed for small defect objects during training to improve detection accuracy.Experiments show the average precision and average precision for small objects are 42.7%and 33.5%at a detection speed of 62 frames per second on a single GPU,respectively.This shows that DLF-YOLOF has excellent performance to meet the needs of industrial real-time detection.展开更多
As a GIS tool,visibility analysis is used in many areas to evaluate both visible and non-visible places.Visibility analysis builds on a digital surface model describing the terrain morphology,including the position an...As a GIS tool,visibility analysis is used in many areas to evaluate both visible and non-visible places.Visibility analysis builds on a digital surface model describing the terrain morphology,including the position and shapes of all objects that can sometimes act as visibility barriers.However,some barriers,for example vegetation,may be permeable to a certain degree.Despite extensive research and use of visibility analysis in different areas,standard GIS tools do not take permeability into account.This article presents a new method to calculate visibility through partly permeable obstacles.The method is based on a quasi-Monte Carlo simulation with 100 iterations of visibility calculation.Each iteration result represents 1%of vegetation permeability,which can thus range from 1%to 100%visibility behind vegetation obstacles.The main advantage of the method is greater accuracy of visibility results and easy implementation on any GIS software.The incorporation of the proposed method in GIS software would facilitate work in many fields,such as architecture,archaeology,radio communication,and the military.展开更多
Segment Anything Model(SAM)is a cutting-edge model that has shown impressive performance in general object segmentation.The birth of the segment anything is a groundbreaking step towards creating a universal intellige...Segment Anything Model(SAM)is a cutting-edge model that has shown impressive performance in general object segmentation.The birth of the segment anything is a groundbreaking step towards creating a universal intelligent model.Due to its superior performance in general object segmentation,it quickly gained attention and interest.This makes SAM particularly attractive in industrial surface defect segmentation,especially for complex industrial scenes with limited training data.However,its segmentation ability for specific industrial scenes remains unknown.Therefore,in this work,we select three representative and complex industrial surface defect detection scenarios,namely strip steel surface defects,tile surface defects,and rail surface defects,to evaluate the segmentation performance of SAM.Our results show that although SAM has great potential in general object segmentation,it cannot achieve satisfactory performance in complex industrial scenes.Our test results are available at:https://github.com/VDT-2048/SAM-IS.展开更多
Printed Circuit Board(PCB)surface tiny defect detection is a difficult task in the integrated circuit industry,especially since the detection of tiny defects on PCB boards with large-size complex circuits has become o...Printed Circuit Board(PCB)surface tiny defect detection is a difficult task in the integrated circuit industry,especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks.To improve the performance of PCB surface tiny defects detection,a PCB tiny defects detection model based on an improved attention residual network(YOLOX-AttResNet)is proposed.First,the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet(Squeeze and Excitation Network)attention network;then the improved K-means-SENet network is fused with the directly mapped edges of the traditional ResNet network to form an augmented residual network(AttResNet);and finally,the AttResNet module is substituted for the traditional ResNet structure in the backbone feature extraction network of mainstream excellent detection models,thus improving the ability to extract small features from the backbone of the target detection network.The results of ablation experiments on a PCB surface defect dataset show that AttResNet is a reliable and efficient module.In Torify the performance of AttResNet for detecting small defects in large-size complex circuit images,a series of comparison experiments are further performed.The results show that the AttResNet module combines well with the five best existing target detection frameworks(YOLOv3,YOLOX,Faster R-CNN,TDD-Net,Cascade R-CNN),and all the combined new models have improved detection accuracy compared to the original model,which suggests that the AttResNet module proposed in this paper can help the detection model to extract target features.Among them,the YOLOX-AttResNet model proposed in this paper performs the best,with the highest accuracy of 98.45% and the detection speed of 36 FPS(Frames Per Second),which meets the accuracy and real-time requirements for the detection of tiny defects on PCB surfaces.This study can provide some new ideas for other real-time online detection tasks of tiny targets with high-resolution images.展开更多
Coal is the primary energy resource in China. Thousands of underground coal mines are operating in China and cause severe land subsidence, leading to many environmental and engineering problems. Huainan （淮南） coal ...Coal is the primary energy resource in China. Thousands of underground coal mines are operating in China and cause severe land subsidence, leading to many environmental and engineering problems. Huainan （淮南） coal mine is the largest coal mining area in East China. Surface subsidence associated with Huainan coal mining activities has been monitoring by DInSAR （differential synthetic aperture radar） techniques in this study. Four ASAR （advanced SAR） pairs from 2009 to 2010 are selected to perform 2-pass DInSAR processing with spatial and temporal baselines suitable for subsidence monitoring. The subsidence maps generated from these pairs show that the extension of subsidence is consistent with the field observation. Quantitative measurements indicated that the magnitudes of subsidence are increased with the development of underground coal mining exploitation. This study demonstrates that DInSAR technique is effective for surface subsidence monitoring in coal mining area. Limitations and recommendations both in the adopted method and auxiliary data are also discussed.展开更多
Impervious surfaces are the result of urbanization that can be explicitly quantified, managed and controlled at each stage of land development. It is a very useful environmental indicator that can be used to measure t...Impervious surfaces are the result of urbanization that can be explicitly quantified, managed and controlled at each stage of land development. It is a very useful environmental indicator that can be used to measure the impacts of urbanization on surface runoff, water quality, air quality, biodiversity and rnicroclimate. Therefore, accurate estimation of impervious surfaces is critical for urban environmental monitoring, land management, decision-making and urban planning. Many approaches have been developed to estimate surface imperviousness, using remotely sensed data with various spatial resolutions. However, few studies, have investigated the effects of spatial resolution on estimating surface imperviousness. We compare medium-resolution Landsat data with high-resolution SPOT images to quantify the imperviousness in Beijing, China. The results indicated that the overall 91% accuracy of estimates of imperviousness based on TM data was considerably higher than the 81% accuracy of the SPOT data. The higher resolution SPOT data did not always predict the imperviousness of the land better than the TM data. At the whole city level, the TM data better predicts the percentage cover of impervious surfaces. At the sub-city level, however, the ring belts from the central core to the urban-rural peripheral, the SPOT data may better predict the imperviousness. These results highlighted the need to combine multiple resolution data to quantify the percentage of imperviousness, as higher resolution data do not necessarily lead to more accurate estimates. The methodology and results in this study can be utilized to identify the most suitable remote sensing data to quickly and efficiently extract the pattern of the impervious land, which could provide the base for further study on many related urban environmental problems.展开更多
The choice of the UHV lines depends on surface electric field of the bundle conductors.Based on existing calculation methods,the optimized charge simulation method is used to calculate the conductors' surface elec...The choice of the UHV lines depends on surface electric field of the bundle conductors.Based on existing calculation methods,the optimized charge simulation method is used to calculate the conductors' surface electrical field of±800 kV UHVDC transmission lines in this paper.During calculation,the offset distance is set as the variance of the objective function,the position and the quantity of the simulation charges are optimized with the gold section method,and the surface electrical field is calculated when the charge is in the optimal position.The result shows that the distribution of the surface electrical field and its maximal value can be calculated accurately with this method,although less number of simulation charges is used in this proposed method and the calculation is simple.展开更多
Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface ...Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.展开更多
In this paper, a new type of water floater fishing machine is designed, including the hull, collecting device, fishing device and other structural design. It can complete deal with the water floater fishing and compre...In this paper, a new type of water floater fishing machine is designed, including the hull, collecting device, fishing device and other structural design. It can complete deal with the water floater fishing and compression. It has the characteristics of easy operation and strong mobility, and has a wide range of application value.展开更多
Real-time detection of surface defects on cables is crucial for ensuring the safe operation of power systems.However,existing methods struggle with small target sizes,complex backgrounds,low-quality image acquisition,...Real-time detection of surface defects on cables is crucial for ensuring the safe operation of power systems.However,existing methods struggle with small target sizes,complex backgrounds,low-quality image acquisition,and interference from contamination.To address these challenges,this paper proposes the Real-time Cable Defect Detection Network(RC2DNet),which achieves an optimal balance between detection accuracy and computational efficiency.Unlike conventional approaches,RC2DNet introduces a small object feature extraction module that enhances the semantic representation of small targets through feature pyramids,multi-level feature fusion,and an adaptive weighting mechanism.Additionally,a boundary feature enhancement module is designed,incorporating boundary-aware convolution,a novel boundary attention mechanism,and an improved loss function to significantly enhance boundary localization accuracy.Experimental results demonstrate that RC2DNet outperforms state-of-the-art methods in precision,recall,F1-score,mean Intersection over Union(mIoU),and frame rate,enabling real-time and highly accurate cable defect detection in complex backgrounds.展开更多
基金supported in part by the National Natural Science Foundation of China(Grant No.62071123)in part by the Natural Science Foundation of Fujian Province(Grant Nos.2024J01971,2022J05202)in part by the Young and Middle-Aged Teacher Education Research Project of Fujian Province(Grant No.JAT210370).
文摘In industrial manufacturing,efficient surface defect detection is crucial for ensuring product quality and production safety.Traditional inspectionmethods are often slow,subjective,and prone to errors,while classicalmachine vision techniques strugglewith complex backgrounds and small defects.To address these challenges,this study proposes an improved YOLOv11 model for detecting defects on hot-rolled steel strips using the NEU-DET dataset.Three key improvements are introduced in the proposed model.First,a lightweight Guided Attention Feature Module(GAFM)is incorporated to enhance multi-scale feature fusion,allowing the model to better capture and integrate semantic and spatial information across different layers,which improves its ability to detect defects of varying sizes.Second,an Aggregated Attention(AA)mechanism is employed to strengthen the representation of critical defect features while effectively suppressing irrelevant background information,particularly enhancing the detection of small,low-contrast,or complex defects.Third,Ghost Dynamic Convolution(GDC)is applied to reduce computational cost by generating low-cost ghost features and dynamically reweighting convolutional kernels,enabling faster inference without sacrificing feature quality or detection accuracy.Extensive experiments demonstrate that the proposed model achieves a mean Average Precision(mAP)of 87.2%,compared to 81.5%for the baseline,while lowering computational cost from6.3Giga Floating-point Operations Per Second(GFLOPs)to 5.1 GFLOPs.These results indicate that the improved YOLOv11 is both accurate and computationally efficient,making it suitable for real-time industrial surface defect detection and contributing to the development of practical,high-performance inspection systems.
基金supported in part by the National Key R&D Program of China(Grant No.2023YFB3307604)the Shanxi Province Basic Research Program Youth Science Research Project(Grant Nos.202303021212054 and 202303021212046)+3 种基金the Key Projects Supported by Hebei Natural Science Foundation(Grant No.E2024203125)the National Science Foundation of China(Grant No.52105391)the Hebei Provincial Science and Technology Major Project(Grant No.23280101Z)the National Key Laboratory of Metal Forming Technology and Heavy Equipment Open Fund(Grant No.S2308100.W17).
文摘A novel dual-branch decoding fusion convolutional neural network model(DDFNet)specifically designed for real-time salient object detection(SOD)on steel surfaces is proposed.DDFNet is based on a standard encoder–decoder architecture.DDFNet integrates three key innovations:first,we introduce a novel,lightweight multi-scale progressive aggregation residual network that effectively suppresses background interference and refines defect details,enabling efficient salient feature extraction.Then,we propose an innovative dual-branch decoding fusion structure,comprising the refined defect representation branch and the enhanced defect representation branch,which enhance accuracy in defect region identification and feature representation.Additionally,to further improve the detection of small and complex defects,we incorporate a multi-scale attention fusion module.Experimental results on the public ESDIs-SOD dataset show that DDFNet,with only 3.69 million parameters,achieves detection performance comparable to current state-of-the-art models,demonstrating its potential for real-time industrial applications.Furthermore,our DDFNet-L variant consistently outperforms leading methods in detection performance.The code is available at https://github.com/13140W/DDFNet.
基金National Natural Science Foundation of China(40771147)Global Change Research Projects of Key National Scientific Research Plan(2010CB951302)the Social Commonweal Meteorological Research Project(GYHY201106027)
文摘The field spectroradiometer was used to measure spectra of different snow and snow-covered land surface objects in Beijing area.The result showed that for a pure snow spectrum,the snow reflectance peaks appeared from visible to 800 nm band locations;there was an obvious absorption valley of snow spectrum near 1 030 nm wavelength.Compared with fresh snow,the reflection peaks of the old snow and melting snow showed different degrees of decline in the ranges of 300～1 300,1 700～1 800 and 2 200～2 300 nm,the lowest was from the compacted snow and frozen ice.For the vegetation and snow mixed spectral characteristics,it was indicated that the spectral reflectance increased for the snow-covered land types(including pine leaf with snow and pine leaf on snow background), due to the influence of snow background in the range of 350～1 300 nm.However, the spectrum reflectance of mixed pixel remained a vegetation spectral characteristic.In the end,based on the spectrum analysis of snow,vegetation,and mixed snow/vegetation pixels,the mixed spectral fitting equations were established,and the results showed that there was good correlation between spectral curves by simulation fitting and observed ones(correlation coefficient R2=0.950 9).
基金Program for Young Excellent Talents in the University of Fujian Province,Grant/Award Number:201847The National Key Research and Development Program of China,Grant/Award Number:2022YFB3206605Natural Science Foundation of Xiamen Municipality,Grant/Award Number:3502Z20227189。
文摘Efficient detection of surface defects is primary for ensuring product quality during manufacturing processes.To enhance the performance of deep learning-based methods in practical applications,the authors propose Dense-YOLO,a fast surface defect detection network that combines the strengths of DenseNet and you only look once version 3(YOLOv3).The authors design a lightweight backbone network with improved densely connected blocks,optimising the utilisation of shallow features while maintaining high detection speeds.Additionally,the authors refine the feature pyramid network of YOLOv3 to increase the recall of tiny defects and overall positioning accuracy.Furthermore,an online multi-angle template matching technique is introduced based on normalised cross-correlation to precisely locate the detection area.This refined template matching method not only accelerates detection speed but also mitigates the influence of the background.To validate the effectiveness of our enhancements,the authors conduct comparative experiments across two private datasets and one public dataset.Results show that Dense-YOLO outperforms existing methods,such as faster R-CNN,YOLOv3,YOLOv5s,YOLOv7,and SSD,in terms of mean average precision(mAP)and detection speed.Moreover,Dense-YOLO outperforms networks inherited from VGG and ResNet,including improved faster R-CNN,FCOS,M2Det-320 and FRCN,in mAP.
基金supported by the Earth Observation Research Department,Centre des Techniques Spatiales(CTS),Algerian Space Agency(ASAL)。
文摘Exploiting remote sensing data is a promising approach to estimate surface solar irradiance(SSI).In this study,we propose a method to estimate global SSI using a lookup table and Landsat data.Despite the low temporal resolution of the data used,the developed method produces SSI maps with adequate spatial resolution.It combines physical parameters extracted from Landsat metadata files with the physical laws governing global solar irradiance,its transmission through the atmosphere,and surface reflectance.The results obtained are compared with those in the literature,particularly one study that uses Meteosat data and two others that use radiometric spectral and temporal models.Additionally,experiments are conducted at three sites in Algeria:Oran,In Amenas,and Tamenghasset.The findings indicate that the proposed approach aligns with the tested literature methods while providing SSI maps with superior spatial resolution.Furthermore,the obtained solar irradiances exhibit a root mean square error of approximately 190 W m^(−2)μm^(−1) compared with those of the Bird and Riordan spectral model,and approximately 50 W m^(−2) compared with the results from the Bird and Hulstrom temporal model,and are also comparable to the results of previous studies.
基金supported by the Jiangsu Province Science and Technology Policy Guidance Program(Industry-University-Research Cooperation)/Forward-Looking Joint Research Project(BY2016005-05).
文摘In response to themissed and false detections that are easily caused by the large variety of and significant differences among aluminum surface defects,a detection algorithm based on an improved You Only Look Once(YOLO)v8n network is proposed.First,a C2f_DWR_DRB module is constructed by introducing a dilation-wise residual(DWR)module and a dilated reparameterization block(DRB)to replace the C2f module at the high level of the backbone network,enriching the gradient flow information and increasing the effective receptive field(ERF).Second,an efficient local attention(ELA)mechanism is fused with the high-level screening-feature pyramid networks(HS-FPN)module,and an ELA_HSFPN is designed to replace the original feature fusion module,enhancing the ability of the network to cope with multiscale detection tasks.Moreover,a lightweight shared convolutional detection head(SCDH)is introduced to reduce the number of parameters and the computational complexity of the module while enhancing the performance and generalizability of the model.Finally,the soft intersection over union(SIoU)replaces the original loss function to improve the convergence speed and prediction accuracy of the model.Experimental results show that compared with that of the original YOLOv8n model,the mAP@0.5 of the improved algorithm is increased by 5.1%,the number of parameters and computational complexity are reduced by 33.3%and 32.1%,respectively,and the FPS is increased by 4.9%.Compared with other mainstream object detection algorithms,the improved algorithm still leads in terms of core indicators and has good generalizability for surface defects encountered in other industrial scenarios.
文摘The optimum friction welding （FW） parameters of duplex stainless steel （DSS） UNS $32205 joint was determined. The experiment was carried out as the central composite array of 30 experiments. The selected input parameters were friction pressure （F）, upset pressure （U）, speed （S） and burn-off length （B）, and responses were hardness and ultimate tensile strength. To achieve the quality of the welded joint, the ultimate tensile strength and hardness were maximized, and response surface methodology （RSM） was applied to create separate regression equations of tensile strength and hardness. Intelligent optimization technique such as genetic algorithm was used to predict the Pareto optimal solutions. Depending upon the application, preferred suitable welding parameters were selected. It was inferred that the changing hardness and tensile strength of the friction welded joint influenced the upset pressure, friction Pressure and speed of rotation.
文摘Since the volume transport across the pycnocline is much smaller than that in the mixed layer, the current in the mixed layer can be regarded as non-divergent. An objective analysis method is deduced based on this hypothesis. The linear combination method is used to solve the non-divergent component of the current field of an ocean basin containing islands,which is equivalent to a mathematical problem of solving a Poisson equation in a multi-connected domain. The method is applied to the Bohai Sea, the Yellow Sea and the East China Sea (ECS). The modeled result is consistent with the current maps constructed by other oceanographers.
基金supported by the Natural Science Foundation of Liaoning Province(No.2022-MS-353)Basic Scientific Research Project of Education Department of Liaoning Province(Nos.2020LNZD06 and LJKMZ20220640)。
文摘Surface defects can affect the quality of steel plate.Many methods based on computer vision are currently applied to surface defect detection of steel plate.However,their real-time performance and object detection of small defect are still unsatisfactory.An improved object detection network based on You Only Look One-level Feature(YOLOF)is proposed to show excellent performance in surface defect detection of steel plate,called DLF-YOLOF.First,the anchor-free detector is used to reduce the network hyperparameters.Secondly,deformable convolution network and local spatial attention module are introduced into the feature extraction network to increase the contextual information in the feature maps.Also,the soft non-maximum suppression is used to improve detection accuracy significantly.Finally,data augmentation is performed for small defect objects during training to improve detection accuracy.Experiments show the average precision and average precision for small objects are 42.7%and 33.5%at a detection speed of 62 frames per second on a single GPU,respectively.This shows that DLF-YOLOF has excellent performance to meet the needs of industrial real-time detection.
基金This work was financially supported by project 133/2016/RPP-TO-1/b“Teaching of advanced techniques for geodata processing for follow-up study of geoinformatics”.
文摘As a GIS tool,visibility analysis is used in many areas to evaluate both visible and non-visible places.Visibility analysis builds on a digital surface model describing the terrain morphology,including the position and shapes of all objects that can sometimes act as visibility barriers.However,some barriers,for example vegetation,may be permeable to a certain degree.Despite extensive research and use of visibility analysis in different areas,standard GIS tools do not take permeability into account.This article presents a new method to calculate visibility through partly permeable obstacles.The method is based on a quasi-Monte Carlo simulation with 100 iterations of visibility calculation.Each iteration result represents 1%of vegetation permeability,which can thus range from 1%to 100%visibility behind vegetation obstacles.The main advantage of the method is greater accuracy of visibility results and easy implementation on any GIS software.The incorporation of the proposed method in GIS software would facilitate work in many fields,such as architecture,archaeology,radio communication,and the military.
基金supported by the National Natural Science Foundation of China(51805078)Project of National Key Laboratory of Advanced Casting Technologies(CAT2023-002)the 111 Project(B16009).
文摘Segment Anything Model(SAM)is a cutting-edge model that has shown impressive performance in general object segmentation.The birth of the segment anything is a groundbreaking step towards creating a universal intelligent model.Due to its superior performance in general object segmentation,it quickly gained attention and interest.This makes SAM particularly attractive in industrial surface defect segmentation,especially for complex industrial scenes with limited training data.However,its segmentation ability for specific industrial scenes remains unknown.Therefore,in this work,we select three representative and complex industrial surface defect detection scenarios,namely strip steel surface defects,tile surface defects,and rail surface defects,to evaluate the segmentation performance of SAM.Our results show that although SAM has great potential in general object segmentation,it cannot achieve satisfactory performance in complex industrial scenes.Our test results are available at:https://github.com/VDT-2048/SAM-IS.
基金supported by the National Natural Science Foundation of China(No.61976083)Hubei Province Key R&D Program of China(No.2022BBA0016).
文摘Printed Circuit Board(PCB)surface tiny defect detection is a difficult task in the integrated circuit industry,especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks.To improve the performance of PCB surface tiny defects detection,a PCB tiny defects detection model based on an improved attention residual network(YOLOX-AttResNet)is proposed.First,the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet(Squeeze and Excitation Network)attention network;then the improved K-means-SENet network is fused with the directly mapped edges of the traditional ResNet network to form an augmented residual network(AttResNet);and finally,the AttResNet module is substituted for the traditional ResNet structure in the backbone feature extraction network of mainstream excellent detection models,thus improving the ability to extract small features from the backbone of the target detection network.The results of ablation experiments on a PCB surface defect dataset show that AttResNet is a reliable and efficient module.In Torify the performance of AttResNet for detecting small defects in large-size complex circuit images,a series of comparison experiments are further performed.The results show that the AttResNet module combines well with the five best existing target detection frameworks(YOLOv3,YOLOX,Faster R-CNN,TDD-Net,Cascade R-CNN),and all the combined new models have improved detection accuracy compared to the original model,which suggests that the AttResNet module proposed in this paper can help the detection model to extract target features.Among them,the YOLOX-AttResNet model proposed in this paper performs the best,with the highest accuracy of 98.45% and the detection speed of 36 FPS(Frames Per Second),which meets the accuracy and real-time requirements for the detection of tiny defects on PCB surfaces.This study can provide some new ideas for other real-time online detection tasks of tiny targets with high-resolution images.
基金supported by the National Key Technology R&D Program of China(No.2012BAC10B02)European Space Agency(No.9389)
文摘Coal is the primary energy resource in China. Thousands of underground coal mines are operating in China and cause severe land subsidence, leading to many environmental and engineering problems. Huainan （淮南） coal mine is the largest coal mining area in East China. Surface subsidence associated with Huainan coal mining activities has been monitoring by DInSAR （differential synthetic aperture radar） techniques in this study. Four ASAR （advanced SAR） pairs from 2009 to 2010 are selected to perform 2-pass DInSAR processing with spatial and temporal baselines suitable for subsidence monitoring. The subsidence maps generated from these pairs show that the extension of subsidence is consistent with the field observation. Quantitative measurements indicated that the magnitudes of subsidence are increased with the development of underground coal mining exploitation. This study demonstrates that DInSAR technique is effective for surface subsidence monitoring in coal mining area. Limitations and recommendations both in the adopted method and auxiliary data are also discussed.
基金supported by the National Basic Research Program (973) of China (No. 2008CB418104)the Major Programs of the Chinese Academy of Sciences (No. KZCX1-YW-14-4-1)the National Natural Science Foundation of China (No. 40901265)
文摘Impervious surfaces are the result of urbanization that can be explicitly quantified, managed and controlled at each stage of land development. It is a very useful environmental indicator that can be used to measure the impacts of urbanization on surface runoff, water quality, air quality, biodiversity and rnicroclimate. Therefore, accurate estimation of impervious surfaces is critical for urban environmental monitoring, land management, decision-making and urban planning. Many approaches have been developed to estimate surface imperviousness, using remotely sensed data with various spatial resolutions. However, few studies, have investigated the effects of spatial resolution on estimating surface imperviousness. We compare medium-resolution Landsat data with high-resolution SPOT images to quantify the imperviousness in Beijing, China. The results indicated that the overall 91% accuracy of estimates of imperviousness based on TM data was considerably higher than the 81% accuracy of the SPOT data. The higher resolution SPOT data did not always predict the imperviousness of the land better than the TM data. At the whole city level, the TM data better predicts the percentage cover of impervious surfaces. At the sub-city level, however, the ring belts from the central core to the urban-rural peripheral, the SPOT data may better predict the imperviousness. These results highlighted the need to combine multiple resolution data to quantify the percentage of imperviousness, as higher resolution data do not necessarily lead to more accurate estimates. The methodology and results in this study can be utilized to identify the most suitable remote sensing data to quickly and efficiently extract the pattern of the impervious land, which could provide the base for further study on many related urban environmental problems.
基金Project Supported by National Natural Science Foundation of China（90510015）.
文摘The choice of the UHV lines depends on surface electric field of the bundle conductors.Based on existing calculation methods,the optimized charge simulation method is used to calculate the conductors' surface electrical field of±800 kV UHVDC transmission lines in this paper.During calculation,the offset distance is set as the variance of the objective function,the position and the quantity of the simulation charges are optimized with the gold section method,and the surface electrical field is calculated when the charge is in the optimal position.The result shows that the distribution of the surface electrical field and its maximal value can be calculated accurately with this method,although less number of simulation charges is used in this proposed method and the calculation is simple.
基金supported by the Future Challenge Program through the Agency for Defense Development funded by the Defense Acquisition Program Administration (No.UC200015RD)。
文摘Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.
文摘In this paper, a new type of water floater fishing machine is designed, including the hull, collecting device, fishing device and other structural design. It can complete deal with the water floater fishing and compression. It has the characteristics of easy operation and strong mobility, and has a wide range of application value.
基金supported by the National Natural Science Foundation of China under Grant 62306128the Basic Science Research Project of Jiangsu Provincial Department of Education under Grant 23KJD520003the Leading Innovation Project of Changzhou Science and Technology Bureau under Grant CQ20230072.
文摘Real-time detection of surface defects on cables is crucial for ensuring the safe operation of power systems.However,existing methods struggle with small target sizes,complex backgrounds,low-quality image acquisition,and interference from contamination.To address these challenges,this paper proposes the Real-time Cable Defect Detection Network(RC2DNet),which achieves an optimal balance between detection accuracy and computational efficiency.Unlike conventional approaches,RC2DNet introduces a small object feature extraction module that enhances the semantic representation of small targets through feature pyramids,multi-level feature fusion,and an adaptive weighting mechanism.Additionally,a boundary feature enhancement module is designed,incorporating boundary-aware convolution,a novel boundary attention mechanism,and an improved loss function to significantly enhance boundary localization accuracy.Experimental results demonstrate that RC2DNet outperforms state-of-the-art methods in precision,recall,F1-score,mean Intersection over Union(mIoU),and frame rate,enabling real-time and highly accurate cable defect detection in complex backgrounds.
