A Surface Defect Detection Method Based on Attention Mechanism for Workpieces

With the rapid development of image processing technology in recent years, faced with the issues of low detection accuracy and missed detections in the process of surface defect detection on workpieces, we propose a workpiece surface defect detection method based on attention mechanism. The model uses EfficientDet-d0 as the baseline network, mixes the Fused-MBConv structure and MBConv structure in EfficientNetv2 network as part of the feature extraction network, and uses the convolutional attention module CBAM to focus on the information of space and channel direction at the same time, and the Hardmish activation function is used in the structure. Introducing a fast spatial pyramid pooling module (SPPF) at the top of the feature extraction network increases the network’s depth and enhances its expressive power. The extracted features are fed into the Improved-BiFPN (Bidirectional Feature Pyramid Network) for feature enhancement, improving the model’s ability to detect defects of different sizes. Experimental results demonstrate that our proposed surface defect detection method for workpieces outperforms other advanced detectors.