Rapid and high-precision speed bump detection is critical for autonomous driving and road safety,yet it faces challenges from non-standard appearances and complex environments.To address this issue,this study proposes...Rapid and high-precision speed bump detection is critical for autonomous driving and road safety,yet it faces challenges from non-standard appearances and complex environments.To address this issue,this study proposes a you only look once(YOLO)algorithm for speed bump detection(SPD-YOLO),a lightweight model based on YOLO11s that integrates three core innova-tive modules to balance detection precision and computational efficiency:it replaces YOLO11s’original backbone with StarNet,which uses‘star operations’to map features into high-dimensional nonlinear spaces for enhanced feature representation while maintaining computational efficiency;its neck incorporates context feature calibration(CFC)and spatial feature calibration(SFC)to improve detection performance without significant computational overhead;and its detection head adopts a lightweight shared convolutional detection(LSCD)structure combined with GroupNorm,minimizing computational complexity while preserving multi-scale feature fusion efficacy.Experi-ments on a custom speed bump dataset show SPD-YOLO achieves a mean average precision(mAP)of 79.9%,surpassing YOLO11s by 1.3%and YOLO12s by 1.2%while reducing parameters by 26.3%and floating-point operations per second(FLOPs)by 29.5%,enabling real-time deploy-ment on resource-constrained platforms.展开更多
To avoid colliding with trees during its operation,a lawn mower robot must detect the trees.Existing tree detection methods suffer from low detection accuracy(missed detection)and the lack of a lightweight model.In th...To avoid colliding with trees during its operation,a lawn mower robot must detect the trees.Existing tree detection methods suffer from low detection accuracy(missed detection)and the lack of a lightweight model.In this study,a dataset of trees was constructed on the basis of a real lawn environment.According to the theory of channel incremental depthwise convolution and residual suppression,the Embedded-A module is proposed,which expands the depth of the feature map twice to form a residual structure to improve the lightweight degree of the model.According to residual fusion theory,the Embedded-B module is proposed,which improves the accuracy of feature-map downsampling by depthwise convolution and pooling fusion.The Embedded YOLO object detection network is formed by stacking the embedded modules and the fusion of feature maps of different resolutions.Experimental results on the testing set show that the Embedded YOLO tree detection algorithm has 84.17%and 69.91%average precision values respectively for trunk and spherical tree,and 77.04% mean average precision value.The number of convolution parameters is 1.78×10^(6),and the calculation amount is 3.85 billion float operations per second.The size of weight file is 7.11MB,and the detection speed can reach 179 frame/s.This study provides a theoretical basis for the lightweight application of the object detection algorithm based on deep learning for lawn mower robots.展开更多
Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and diffe...Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust etc.The pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient envir-onments.In recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set Segmentation.Finally,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore satisfy the true demands of night scene target detection applications.We very much believe that our method will help future research.展开更多
Computer vision-based traffic object detection plays a critical role in road traffic safety.Under hazy weather conditions,images captured by road monitoring systems exhibit three main challenges:significant scale vari...Computer vision-based traffic object detection plays a critical role in road traffic safety.Under hazy weather conditions,images captured by road monitoring systems exhibit three main challenges:significant scale variations,abundant background noise,and diverse perspectives.These factors lead to insufficient detection accuracy and limited real-time performance in object detection algorithms.We propose AMC-YOLO an improved YOLOv11-based traffic detection algorithm to address these challenges.In this work,we replace the C3k block's bottleneck module with our novel attention-gate convolution(AGConv),which improves contextual information capture,enhances feature extraction,and reduces computational redundancy.Additionally,we introduce the multi-dilation sharing convolution(MDSC)module to prevent feature information loss during pooling operations,enhancing the model's sensitivity to multi-scale features.We design a lightweight and efficient cross-channel feature fusion module(CCFM)for the path aggregation neck to adaptively adjust feature weights and optimize the model's overall performance.Experimental results demonstrate that AMC-YOLO achieves a 1.1%improvement in mAP@0.5 and a 2.7%increase in mAP@0.5:0.95 compared to YOLOv11n.On graphics processing unit(GPU)hardware,it achieves real-time performance at 376(FPS)with only 2.6 million parameters,ensuring high-precision traffic detection while meeting deployment requirements on resource-constrained devices.展开更多
Unmanned aerial vehicle(UAV)technology,artificial intelligence,and the relevant hardware can be used for monitoring wild animals.However,existing methods have several limitations.Therefore,this study explored the monit...Unmanned aerial vehicle(UAV)technology,artificial intelligence,and the relevant hardware can be used for monitoring wild animals.However,existing methods have several limitations.Therefore,this study explored the monitoring and protection of Amur tigers and their main prey species using images from UAVs by optimizing the algorithm models with respect to accuracy,model size,recognition speed,and elimination of environmental inter-ference.Thermal imaging data were collected from 2000 pictures with a thermal imaging lens on a DJI M300RTK UAV at the Hanma National Nature Reserve in the Greater Khingan Mountains in Inner Mongolia,Wangqing National Nature Reserve in Jilin Province,and Siberian Tiger Park in Heilongjiang Province.The YOLO V5s al-gorithm was applied to recognize the animals in the pictures.The accuracy rate was 94.1%,and the size of the model weight(total weight of each model layer trained with the training set)was 14.8 MB.The authors improved the structures and parameters of the YOLO V5s algorithm.As a result,the recognition accuracy rate became 96%,and the model weight was 9.3 MB.The accuracy rate increased by 1.9%,the model weight decreased by 37.2%from 14.8 MB to 9.3 MB,and the recognition time of a single picture was shortened by 34.4%from 0.032 to 0.021 s.This not only increases the recognition accuracy but also effectively lowers the hardware requirements that the algorithm relies on,which provides a lightweight fast recognition method for UAV-based edge computing and online investigation of wild animals.展开更多
文摘Rapid and high-precision speed bump detection is critical for autonomous driving and road safety,yet it faces challenges from non-standard appearances and complex environments.To address this issue,this study proposes a you only look once(YOLO)algorithm for speed bump detection(SPD-YOLO),a lightweight model based on YOLO11s that integrates three core innova-tive modules to balance detection precision and computational efficiency:it replaces YOLO11s’original backbone with StarNet,which uses‘star operations’to map features into high-dimensional nonlinear spaces for enhanced feature representation while maintaining computational efficiency;its neck incorporates context feature calibration(CFC)and spatial feature calibration(SFC)to improve detection performance without significant computational overhead;and its detection head adopts a lightweight shared convolutional detection(LSCD)structure combined with GroupNorm,minimizing computational complexity while preserving multi-scale feature fusion efficacy.Experi-ments on a custom speed bump dataset show SPD-YOLO achieves a mean average precision(mAP)of 79.9%,surpassing YOLO11s by 1.3%and YOLO12s by 1.2%while reducing parameters by 26.3%and floating-point operations per second(FLOPs)by 29.5%,enabling real-time deploy-ment on resource-constrained platforms.
基金the National Natural Science Foundation of China (No.51275223)。
文摘To avoid colliding with trees during its operation,a lawn mower robot must detect the trees.Existing tree detection methods suffer from low detection accuracy(missed detection)and the lack of a lightweight model.In this study,a dataset of trees was constructed on the basis of a real lawn environment.According to the theory of channel incremental depthwise convolution and residual suppression,the Embedded-A module is proposed,which expands the depth of the feature map twice to form a residual structure to improve the lightweight degree of the model.According to residual fusion theory,the Embedded-B module is proposed,which improves the accuracy of feature-map downsampling by depthwise convolution and pooling fusion.The Embedded YOLO object detection network is formed by stacking the embedded modules and the fusion of feature maps of different resolutions.Experimental results on the testing set show that the Embedded YOLO tree detection algorithm has 84.17%and 69.91%average precision values respectively for trunk and spherical tree,and 77.04% mean average precision value.The number of convolution parameters is 1.78×10^(6),and the calculation amount is 3.85 billion float operations per second.The size of weight file is 7.11MB,and the detection speed can reach 179 frame/s.This study provides a theoretical basis for the lightweight application of the object detection algorithm based on deep learning for lawn mower robots.
文摘Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust etc.The pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient envir-onments.In recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set Segmentation.Finally,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore satisfy the true demands of night scene target detection applications.We very much believe that our method will help future research.
基金supported by the Wuhan Pilot construction of a strong Transportation Country Science and Technology Joint Research Project(No.2024-1-10).
文摘Computer vision-based traffic object detection plays a critical role in road traffic safety.Under hazy weather conditions,images captured by road monitoring systems exhibit three main challenges:significant scale variations,abundant background noise,and diverse perspectives.These factors lead to insufficient detection accuracy and limited real-time performance in object detection algorithms.We propose AMC-YOLO an improved YOLOv11-based traffic detection algorithm to address these challenges.In this work,we replace the C3k block's bottleneck module with our novel attention-gate convolution(AGConv),which improves contextual information capture,enhances feature extraction,and reduces computational redundancy.Additionally,we introduce the multi-dilation sharing convolution(MDSC)module to prevent feature information loss during pooling operations,enhancing the model's sensitivity to multi-scale features.We design a lightweight and efficient cross-channel feature fusion module(CCFM)for the path aggregation neck to adaptively adjust feature weights and optimize the model's overall performance.Experimental results demonstrate that AMC-YOLO achieves a 1.1%improvement in mAP@0.5 and a 2.7%increase in mAP@0.5:0.95 compared to YOLOv11n.On graphics processing unit(GPU)hardware,it achieves real-time performance at 376(FPS)with only 2.6 million parameters,ensuring high-precision traffic detection while meeting deployment requirements on resource-constrained devices.
基金funded by a program of the Natural Science Foundation of Heilongjiang Province,Research on Key Technologies of Wildlife Intelligent Monitoring(LH2020C034)the National Natural Science Foundation of China(NSFC31872241,32100392)the Fundamental Research Funds for the Central Universities(2572022DS04).
文摘Unmanned aerial vehicle(UAV)technology,artificial intelligence,and the relevant hardware can be used for monitoring wild animals.However,existing methods have several limitations.Therefore,this study explored the monitoring and protection of Amur tigers and their main prey species using images from UAVs by optimizing the algorithm models with respect to accuracy,model size,recognition speed,and elimination of environmental inter-ference.Thermal imaging data were collected from 2000 pictures with a thermal imaging lens on a DJI M300RTK UAV at the Hanma National Nature Reserve in the Greater Khingan Mountains in Inner Mongolia,Wangqing National Nature Reserve in Jilin Province,and Siberian Tiger Park in Heilongjiang Province.The YOLO V5s al-gorithm was applied to recognize the animals in the pictures.The accuracy rate was 94.1%,and the size of the model weight(total weight of each model layer trained with the training set)was 14.8 MB.The authors improved the structures and parameters of the YOLO V5s algorithm.As a result,the recognition accuracy rate became 96%,and the model weight was 9.3 MB.The accuracy rate increased by 1.9%,the model weight decreased by 37.2%from 14.8 MB to 9.3 MB,and the recognition time of a single picture was shortened by 34.4%from 0.032 to 0.021 s.This not only increases the recognition accuracy but also effectively lowers the hardware requirements that the algorithm relies on,which provides a lightweight fast recognition method for UAV-based edge computing and online investigation of wild animals.
