To solve the problem of low detection accuracy for complex weld defects,the paper proposes a weld defects detection method based on improved YOLOv5s.To enhance the ability to focus on key information in feature maps,t...To solve the problem of low detection accuracy for complex weld defects,the paper proposes a weld defects detection method based on improved YOLOv5s.To enhance the ability to focus on key information in feature maps,the scSE attention mechanism is intro-duced into the backbone network of YOLOv5s.A Fusion-Block module and additional layers are added to the neck network of YOLOv5s to improve the effect of feature fusion,which is to meet the needs of complex object detection.To reduce the computation-al complexity of the model,the C3Ghost module is used to replace the CSP2_1 module in the neck network of YOLOv5s.The scSE-ASFF module is constructed and inserted between the neck network and the prediction end,which is to realize the fusion of features between the different layers.To address the issue of imbalanced sample quality in the dataset and improve the regression speed and accuracy of the loss function,the CIoU loss function in the YOLOv5s model is replaced with the Focal-EIoU loss function.Finally,ex-periments are conducted based on the collected weld defect dataset to verify the feasibility of the improved YOLOv5s for weld defects detection.The experimental results show that the precision and mAP of the improved YOLOv5s in detecting complex weld defects are as high as 83.4%and 76.1%,respectively,which are 2.5%and 7.6%higher than the traditional YOLOv5s model.The proposed weld defects detection method based on the improved YOLOv5s in this paper can effectively solve the problem of low weld defects detection accuracy.展开更多
The statistics of the number of rapeseed seedlings are very important for breeders and planters to conduct seed quality testing,field crop management and yield estimation.Calculating the number of seedlings is ineffic...The statistics of the number of rapeseed seedlings are very important for breeders and planters to conduct seed quality testing,field crop management and yield estimation.Calculating the number of seedlings is inefficient and cumbersome in the traditional method.In this study,a method was proposed for efficient detection and calculation of rapeseed seedling number based on improved you only look once version 5(YOLOv5)to identify objects and deep-sort to perform object tracking for rapeseed seedling video.Coordinated attention(CA)mechanism was added to the trunk of the improved YOLOv5s,which made the model more effective in identifying shaded,dense and small rapeseed seedlings.Also,the use of the GSConv module replaced the standard convolution at the neck,reduced model parameters and enabled it better able to be equipped for mobile devices.The accuracy and recall rate of using improved YOLOv5s on the test set by 1.9%and 3.7%compared to 96.2%and 93.7%of YOLOv5s,respectively.The experimental results showed that the average error of monitoring the number of seedlings by unmanned aerial vehicles(UAV)video of rapeseed seedlings based on improved YOLOv5s combined with depth-sort method was 4.3%.The presented approach can realize rapid statistics of the number of rapeseed seedlings in the field based on UAV remote sensing,provide a reference for variety selection and precise management of rapeseed.展开更多
Real-time detection of unhealthy fish remains a significant challenge in intensive recirculating aquaculture.Early recognition of unhealthy fish and the implementation of appropriate treatment measures are crucial for...Real-time detection of unhealthy fish remains a significant challenge in intensive recirculating aquaculture.Early recognition of unhealthy fish and the implementation of appropriate treatment measures are crucial for preventing the spread of diseases and minimizing economic losses.To address this issue,an improved algorithm based on the You Only Look Once v5s(YOLOv5s)lightweight model has been proposed.This enhanced model incorporates a faster lightweight structure and a new Convolutional Block Attention Module(CBAM)to achieve high recognition accuracy.Furthermore,the model introduces theα-SIoU loss function,which combines theα-Intersection over Union(α-IoU)and Shape Intersection over Union(SIoU)loss functions,thereby improving the accuracy of bounding box regression and object recognition.The average precision of the improved model reaches 94.2%for detecting unhealthy fish,representing increases of 11.3%,9.9%,9.7%,2.5%,and 2.1%compared to YOLOv3-tiny,YOLOv4,YOLOv5s,GhostNet-YOLOv5,and YOLOv7,respectively.Additionally,the improved model positively impacts hardware efficiency,reducing requirements for memory size by 59.0%,67.0%,63.0%,44.7%,and 55.6%in comparison to the five models mentioned above.The experimental results underscore the effectiveness of these approaches in addressing the challenges associated with fish health detection,and highlighting their significant practical implications and broad application prospects.展开更多
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_4084).
文摘To solve the problem of low detection accuracy for complex weld defects,the paper proposes a weld defects detection method based on improved YOLOv5s.To enhance the ability to focus on key information in feature maps,the scSE attention mechanism is intro-duced into the backbone network of YOLOv5s.A Fusion-Block module and additional layers are added to the neck network of YOLOv5s to improve the effect of feature fusion,which is to meet the needs of complex object detection.To reduce the computation-al complexity of the model,the C3Ghost module is used to replace the CSP2_1 module in the neck network of YOLOv5s.The scSE-ASFF module is constructed and inserted between the neck network and the prediction end,which is to realize the fusion of features between the different layers.To address the issue of imbalanced sample quality in the dataset and improve the regression speed and accuracy of the loss function,the CIoU loss function in the YOLOv5s model is replaced with the Focal-EIoU loss function.Finally,ex-periments are conducted based on the collected weld defect dataset to verify the feasibility of the improved YOLOv5s for weld defects detection.The experimental results show that the precision and mAP of the improved YOLOv5s in detecting complex weld defects are as high as 83.4%and 76.1%,respectively,which are 2.5%and 7.6%higher than the traditional YOLOv5s model.The proposed weld defects detection method based on the improved YOLOv5s in this paper can effectively solve the problem of low weld defects detection accuracy.
文摘The statistics of the number of rapeseed seedlings are very important for breeders and planters to conduct seed quality testing,field crop management and yield estimation.Calculating the number of seedlings is inefficient and cumbersome in the traditional method.In this study,a method was proposed for efficient detection and calculation of rapeseed seedling number based on improved you only look once version 5(YOLOv5)to identify objects and deep-sort to perform object tracking for rapeseed seedling video.Coordinated attention(CA)mechanism was added to the trunk of the improved YOLOv5s,which made the model more effective in identifying shaded,dense and small rapeseed seedlings.Also,the use of the GSConv module replaced the standard convolution at the neck,reduced model parameters and enabled it better able to be equipped for mobile devices.The accuracy and recall rate of using improved YOLOv5s on the test set by 1.9%and 3.7%compared to 96.2%and 93.7%of YOLOv5s,respectively.The experimental results showed that the average error of monitoring the number of seedlings by unmanned aerial vehicles(UAV)video of rapeseed seedlings based on improved YOLOv5s combined with depth-sort method was 4.3%.The presented approach can realize rapid statistics of the number of rapeseed seedlings in the field based on UAV remote sensing,provide a reference for variety selection and precise management of rapeseed.
基金supported by The Agricultural Science and Technology Independent Innovation Fund Project of Jiangsu Province(CX(22)3111)the National Natural Science Foundation of China Project(62173162)partly by the Changzhou Science and Technology Support Project(CE20225016).
文摘Real-time detection of unhealthy fish remains a significant challenge in intensive recirculating aquaculture.Early recognition of unhealthy fish and the implementation of appropriate treatment measures are crucial for preventing the spread of diseases and minimizing economic losses.To address this issue,an improved algorithm based on the You Only Look Once v5s(YOLOv5s)lightweight model has been proposed.This enhanced model incorporates a faster lightweight structure and a new Convolutional Block Attention Module(CBAM)to achieve high recognition accuracy.Furthermore,the model introduces theα-SIoU loss function,which combines theα-Intersection over Union(α-IoU)and Shape Intersection over Union(SIoU)loss functions,thereby improving the accuracy of bounding box regression and object recognition.The average precision of the improved model reaches 94.2%for detecting unhealthy fish,representing increases of 11.3%,9.9%,9.7%,2.5%,and 2.1%compared to YOLOv3-tiny,YOLOv4,YOLOv5s,GhostNet-YOLOv5,and YOLOv7,respectively.Additionally,the improved model positively impacts hardware efficiency,reducing requirements for memory size by 59.0%,67.0%,63.0%,44.7%,and 55.6%in comparison to the five models mentioned above.The experimental results underscore the effectiveness of these approaches in addressing the challenges associated with fish health detection,and highlighting their significant practical implications and broad application prospects.
