Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially...Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially leading to false positives or missed detections.To solve these problems,the YOLOv8 network is enhanced by adding deformable convolution and atrous spatial pyramid pooling(ASPP),along with the integration of a coordinate attention(CA)mechanism.This allows the network to focus on small targets while expanding the receptive field without losing resolution.At the same time,context information on the target is gathered and feature expression is enhanced by attention modules in different directions.It effectively improves the positioning accuracy and achieves good results on the LUNA16 dataset.Compared with other detection algorithms,it improves the accuracy of pulmonary nodule detection to a certain extent.展开更多
Diseases in tea trees can result in significant losses in both the quality and quantity of tea production.Regular monitoring can help to prevent the occurrence of large-scale diseases in tea plantations.However,existi...Diseases in tea trees can result in significant losses in both the quality and quantity of tea production.Regular monitoring can help to prevent the occurrence of large-scale diseases in tea plantations.However,existingmethods face challenges such as a high number of parameters and low recognition accuracy,which hinders their application in tea plantation monitoring equipment.This paper presents a lightweight I-MobileNetV2 model for identifying diseases in tea leaves,to address these challenges.The proposed method first embeds a Coordinate Attention(CA)module into the originalMobileNetV2 network,enabling the model to locate disease regions accurately.Secondly,a Multi-branch Parallel Convolution(MPC)module is employed to extract disease features across multiple scales,improving themodel’s adaptability to different disease scales.Finally,the AutoML for Model Compression(AMC)is used to compress themodel and reduce computational complexity.Experimental results indicate that our proposed algorithm attains an average accuracy of 96.12%on our self-built tea leaf disease dataset,surpassing the original MobileNetV2 by 1.91%.Furthermore,the number of model parameters have been reduced by 40%,making itmore suitable for practical application in tea plantation environments.展开更多
Neutron radiography is a crucial nondestructive testing technology widely used in the aerospace,military,and nuclear industries.However,because of the physical limitations of neutron sources and collimators,the result...Neutron radiography is a crucial nondestructive testing technology widely used in the aerospace,military,and nuclear industries.However,because of the physical limitations of neutron sources and collimators,the resulting neutron radiographic images inevitably exhibit multiple distortions,including noise,geometric unsharpness,and white spots.Furthermore,these distortions are particularly significant in compact neutron radiography systems with low neutron fluxes.Therefore,in this study,we devised a multi-distortion suppression network that employs a modified generative adversarial network to improve the quality of degraded neutron radiographic images.Real neutron radiographic image datasets with various types and levels of distortion were built for the first time as multi-distortion suppression datasets.Thereafter,the coordinate attention mechanism was incorporated into the backbone network to augment the capability of the proposed network to learn the abstract relationship between ideally clear and degraded images.Extensive experiments were performed;the results show that the proposed method can effectively suppress multiple distortions in real neutron radiographic images and achieve state-of-theart perceptual visual quality,thus demonstrating its application potential in neutron radiography.展开更多
Accurate detection of citrus can be easily affected by adjacent branches and overlapped fruits in natural orchard conditions,where some specific information of citrus might be lost due to the resultant complex occlusi...Accurate detection of citrus can be easily affected by adjacent branches and overlapped fruits in natural orchard conditions,where some specific information of citrus might be lost due to the resultant complex occlusion.Traditional deep learning models might result in lower detection accuracy and detection speed when facing occluded targets.To solve this problem,an improved deep learning algorithm based on YOLOv5,named IYOLOv5,was proposed for accurate detection of citrus fruits.An innovative Res-CSPDarknet network was firstly employed to both enhance feature extraction performance and minimize feature loss within the backbone network,which aims to reduce the miss detection rate.Subsequently,the BiFPN module was adopted as the new neck net to enhance the function for extracting deep semantic features.A coordinate attention mechanism module was then introduced into the network’s detection layer.The performance of the proposed model was evaluated on a home-made citrus dataset containing 2000 optical images.The results show that the proposed IYOLOv5 achieved the highest mean average precision(93.5%)and F1-score(95.6%),compared to the traditional deep learning models including Faster R-CNN,CenterNet,YOLOv3,YOLOv5,and YOLOv7.In particular,the proposed IYOLOv5 obtained a decrease of missed detection rate(at least 13.1%)on the specific task of detecting heavily occluded citrus,compared to other models.Therefore,the proposed method could be potentially used as part of the vision system of a picking robot to identify the citrus fruits accurately.展开更多
文摘Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially leading to false positives or missed detections.To solve these problems,the YOLOv8 network is enhanced by adding deformable convolution and atrous spatial pyramid pooling(ASPP),along with the integration of a coordinate attention(CA)mechanism.This allows the network to focus on small targets while expanding the receptive field without losing resolution.At the same time,context information on the target is gathered and feature expression is enhanced by attention modules in different directions.It effectively improves the positioning accuracy and achieves good results on the LUNA16 dataset.Compared with other detection algorithms,it improves the accuracy of pulmonary nodule detection to a certain extent.
基金supported by National Key Research and Development Program(No.2016YFD0201305-07)Guizhou Provincial Basic Research Program(Natural Science)(No.ZK[2023]060)Open Fund Project in Semiconductor Power Device Reliability Engineering Center of Ministry of Education(No.ERCMEKFJJ2019-06).
文摘Diseases in tea trees can result in significant losses in both the quality and quantity of tea production.Regular monitoring can help to prevent the occurrence of large-scale diseases in tea plantations.However,existingmethods face challenges such as a high number of parameters and low recognition accuracy,which hinders their application in tea plantation monitoring equipment.This paper presents a lightweight I-MobileNetV2 model for identifying diseases in tea leaves,to address these challenges.The proposed method first embeds a Coordinate Attention(CA)module into the originalMobileNetV2 network,enabling the model to locate disease regions accurately.Secondly,a Multi-branch Parallel Convolution(MPC)module is employed to extract disease features across multiple scales,improving themodel’s adaptability to different disease scales.Finally,the AutoML for Model Compression(AMC)is used to compress themodel and reduce computational complexity.Experimental results indicate that our proposed algorithm attains an average accuracy of 96.12%on our self-built tea leaf disease dataset,surpassing the original MobileNetV2 by 1.91%.Furthermore,the number of model parameters have been reduced by 40%,making itmore suitable for practical application in tea plantation environments.
基金supported by National Natural Science Foundation of China(Nos.11905028,12105040)Scientific Research Project of Education Department of Jilin Province(No.JJKH20231294KJ)。
文摘Neutron radiography is a crucial nondestructive testing technology widely used in the aerospace,military,and nuclear industries.However,because of the physical limitations of neutron sources and collimators,the resulting neutron radiographic images inevitably exhibit multiple distortions,including noise,geometric unsharpness,and white spots.Furthermore,these distortions are particularly significant in compact neutron radiography systems with low neutron fluxes.Therefore,in this study,we devised a multi-distortion suppression network that employs a modified generative adversarial network to improve the quality of degraded neutron radiographic images.Real neutron radiographic image datasets with various types and levels of distortion were built for the first time as multi-distortion suppression datasets.Thereafter,the coordinate attention mechanism was incorporated into the backbone network to augment the capability of the proposed network to learn the abstract relationship between ideally clear and degraded images.Extensive experiments were performed;the results show that the proposed method can effectively suppress multiple distortions in real neutron radiographic images and achieve state-of-theart perceptual visual quality,thus demonstrating its application potential in neutron radiography.
基金supported in part by the Natural Science Foundation of Guangdong Province,China(Grant No.2020B1515120070,Grant No.2022A1515010885)the Innovation Team Project of Universities in Guangdong Province,China(Grant No.2021KCXTD010)+2 种基金the Key Construction Discipline Research Capacity Enhancement Project of Guangdong Province,China(Grant No.2022ZDJS014)the Key Construction Discipline Research Capacity Enhancement Project of GPNU,China(Grant No.22GPNUZDJS11)the Characteristic Innovation Project of Universities in Guangdong Province,China(Grant No.2023KTSCX066).
文摘Accurate detection of citrus can be easily affected by adjacent branches and overlapped fruits in natural orchard conditions,where some specific information of citrus might be lost due to the resultant complex occlusion.Traditional deep learning models might result in lower detection accuracy and detection speed when facing occluded targets.To solve this problem,an improved deep learning algorithm based on YOLOv5,named IYOLOv5,was proposed for accurate detection of citrus fruits.An innovative Res-CSPDarknet network was firstly employed to both enhance feature extraction performance and minimize feature loss within the backbone network,which aims to reduce the miss detection rate.Subsequently,the BiFPN module was adopted as the new neck net to enhance the function for extracting deep semantic features.A coordinate attention mechanism module was then introduced into the network’s detection layer.The performance of the proposed model was evaluated on a home-made citrus dataset containing 2000 optical images.The results show that the proposed IYOLOv5 achieved the highest mean average precision(93.5%)and F1-score(95.6%),compared to the traditional deep learning models including Faster R-CNN,CenterNet,YOLOv3,YOLOv5,and YOLOv7.In particular,the proposed IYOLOv5 obtained a decrease of missed detection rate(at least 13.1%)on the specific task of detecting heavily occluded citrus,compared to other models.Therefore,the proposed method could be potentially used as part of the vision system of a picking robot to identify the citrus fruits accurately.
