Defect detection algorithm of lightweight chip based on improved YOLOv5s

Abstract

In modern chip industry production, the existing object detection algorithm parameters are large in number and complex in structure, which is gradually failed to meet the high standards in the production of contemporary enterprises, for the above testing problem, the defect detection algorithm of lightweight chip based on improved YOLOv5s was proposed. First, make improvements on the anchors clustering algorithm to change the definition of the distance between the sample data, and the feature extraction mechanism of ShuffleNetv2 is introduced in the backbone network, reduced the calculation of parameters, at last, the loss function of Focal-EIOU is used, which enhanced the detection performance of YOLOv5s network for various chip defects. The mAP_0.5 of the modified network can reach 87.6% on the provided chip dataset, compared with other network structures there are obvious improvements, and the number of network parameters decreased by 39.7%, which fully proves that the improved network can reach the high standard and lightweight in chip defect detection.