Research on printed circuit boards defect detection based on fusion dynamic convolutional DiffusionDet

Printed circuit board defects will directly affect the performance of electronic equipment. The industry has been troubled by how to effectively detect the defects of printed circuit boards. To solve this problem, an improved algorithm based on DiffusionDet is proposed to detect PCB defects directly without relying on the query of experience objects or learnable objects. The specific processing process is as follows: firstly, Object Detection Convolution (ODConv) is used to replace part of the static convolution kernel of the feature extraction network to broaden the dimension of convolutional operation learning, so as to obtain feature maps that are more conducive to defect detection. Secondly, the GIoU loss function is used to replace the original IoU loss function to improve the model's ability to optimize the position of preselected frames. The experimental results show that the improved DiffusionDet average accuracy (mAP) is increased from 98.12% to 98.83%, showing better performance in the detection of printed circuit plate defects.