With the rapid development of smart manufacturing,intelligent safety monitoring in industrial workshops has become increasingly important.To address the challenges of complex backgrounds,target scale variation,and exc...With the rapid development of smart manufacturing,intelligent safety monitoring in industrial workshops has become increasingly important.To address the challenges of complex backgrounds,target scale variation,and excessive model parameters in worker violation detection,this study proposes ADCP-YOLO,an enhanced lightweight model based on YOLOv8.Here,“ADCP”represents four key improvements:Alterable Kernel Convolution(AKConv),Dilated-Wise Residual(DWR)module,Channel Reconstruction Global Attention Mechanism(CRGAM),and Powerful-IoU loss.These components collaboratively enhance feature extraction,multi-scale perception,and localization accuracy while effectively reducing model complexity and computational cost.Experimental results show that ADCP-YOLO achieves a mAP of 90.6%,surpassing YOLOv8 by 3.0%with a 6.6%reduction in parameters.These findings demonstrate that ADCP-YOLO successfully balances accuracy and efficiency,offering a practical solution for intelligent safety monitoring in smart factory workshops.展开更多
基金TheNationalNatural Science Foundation ofChina(Nos.62272418,62102058)Zhejiang Provincial Natural Science Foundation Major Project(No.LD24F020004)the Major Open Project of Key Laboratory for Advanced Design and Intelligent Computing of the Ministry of Education(No.ADIC2023ZD001).
文摘With the rapid development of smart manufacturing,intelligent safety monitoring in industrial workshops has become increasingly important.To address the challenges of complex backgrounds,target scale variation,and excessive model parameters in worker violation detection,this study proposes ADCP-YOLO,an enhanced lightweight model based on YOLOv8.Here,“ADCP”represents four key improvements:Alterable Kernel Convolution(AKConv),Dilated-Wise Residual(DWR)module,Channel Reconstruction Global Attention Mechanism(CRGAM),and Powerful-IoU loss.These components collaboratively enhance feature extraction,multi-scale perception,and localization accuracy while effectively reducing model complexity and computational cost.Experimental results show that ADCP-YOLO achieves a mAP of 90.6%,surpassing YOLOv8 by 3.0%with a 6.6%reduction in parameters.These findings demonstrate that ADCP-YOLO successfully balances accuracy and efficiency,offering a practical solution for intelligent safety monitoring in smart factory workshops.
