Abstract
For the requirements of automatic testing in the IC industry, an improved algorithm based on YOLOv5 is proposed to identify IC. Firstly, the feature enhancement of small targets is based on the structure of the feature pyramid (FPN) to improve the accuracy of small target detection. Secondly, the weight of the loss value of the wrong sample in the loss function is enhanced to improve the training efficiency of the model. Finally, through the verification of the dataset, the results show that the accuracy of target recognition of the improved model is improved to 99.8% compared with the existing model, which verifies the effectiveness of this method.
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