PCB Surface Defect Detection Based on Improved YOLOv7-tiny
Realizing real-time printed circuit board(PCB)surface defect detection is the basis for improving the intelligence of the PCB fabrication process.Aiming at the original PCB inspection method which is time-consuming,low-accuracy and not easy to move,this paper proposes an improved model based on YOLOv7-tiny.Replace the SiLU activation function in YOLOv7-tiny with the ELU function;introduce a centralized integrated convolutional module(C3 module),and combine depthwise separable convolution with C3 to form a centralized integrated depthwise separable convolution module;and add a convolutional block attention module.Experimentally,the improved model performs well in detection accuracy,recall rate,and mean average precision,and the size of the model drops by 2.8 MB compared to the original model.It also shows better detection results when compared with other mainstream target detection schemes.The ability of the improved YOLOv7-tiny to maintain higher accuracy while also reducing the memory requirements of the model opens up new possibilities for real-time detection of PCB defects as well as edge deployment.
国家科技期刊平台
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