Chip appearance defect detection based on RT-YOLO-V5
Aiming at the problems caused by traditional manual chip detection,with low efficiency,excessive dependence on human operation and high misdetection rate,an RT-YOLO-V5 detection method was proposed to detect chip appearance defects based on the Res-CBS module and an additional Tiny-scale detection layer.First of all,an image acquisition system was built,and a chip appearance defect detection dataset was produced.Due to the de-fects are irregular in shape,inconsistent in size and uncertain in location,the performance of YOLO-V5 network can no longer meet the detection requirements.A short connection was added to the CBS module,fusing the fea-ture information of input and output,reducing the information loss and optimizing the inference speed.In addi-tion,a tiny-scale detection layer is added as well,to improve the feature extraction capability of the model for tiny targets.The experimental results show that using the improved network for chip appearance defect detection,mAP reached 95.5％,which was a 5.7％improvement compared to the original network.In addition,the im-proved RT-YOLO-V5 has gained some improvement in both Box_loss and the accuracy of tiny-scale defect de-tection.
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