基于GDPC-YOLOv7的螺栓和线夹锈蚀检测方法OA

Addressing the issues of complex backgrounds and low detection accuracy in corrosion de-tection for bolt and clamp on transmission lines in mining areas,this paper proposes a lightweight detection algorithm named GDPC-YOLOv7.Firstly,a GS-ELAN module is constructed by introducing the GSBot-tleneck module and integrating it with the original ELAN module,thereby streamlining the backbone net-work and reducing the model's parameter count.Secondly,a DW-SPPCSPC module is designed,where the standard convolution in the SPPCSPC module is replaced with Depthwise Separable Convolutions(DW-Conv).This ensures the model's feature extraction capability while decreasing its parameters.Thirdly,a P-ELAN module is constructed by pruning the ELAN-H module within the network and introducing Partial Convolution(PConv),which further reduces both the parameter count and computational overhead.Finally,a Coordinate Attention(CA)mechanism is embedded into the neck network to enhance the model's focus on crucial features and improve detection accuracy.Experimental results demonstrate that the GDPC-YOLOv7 algorithm achieves a mean Average Precision(mAP)of 91.3%,representing an improvement of 1.85 percentage points over the original network.The parameter number is reduced from 49.25×106 to 19.69×106,and the frame rate from 58.47 frames/s to 68.96 frames/s.The improved model effectively balances the detection accuracy and speed,meeting the requirements for corrosion detection of bolt and clamps on transmission lines in mining areas.