Deep learning-based surface defect classification of scrap circuit boards
A classification method using deep learning is proposed for the recognition of surface defects on scrap circuit boards.Based on the ResNet34 model,the CBAM attention mechanism is added.By combining the ResNet34 architecture with the attention mechanism,it aims to enhance the model's ability to focus on important features in the input image and prioritize them,thus improving the classification accuracy.The results demonstrate the effectiveness of the proposed method in improving the clas-sification of surface defects on scrap circuit boards.
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