A multi-attention fusion mechanism for collaborative industrial surface defect detection

The surface defects of industrial materials can seriously affect product quality, so that the industry has the demand for high precision defect detection algorithms. Towards this issue, we investigate the methods of defect detection accuracy improvement and collaborative training. This paper first innovatively proposes the multi-attention fusion mechanism (MAF), which integrates both channel and space dimensions, and embeds the spatial pyramid structure into the attention module. It alleviates the problem of inconspicuous defective features and enhances the feature extraction ability. Secondly, this paper proposes the mixForm data augmentation algorithm to transform the target defects in space and shape to tackle the problem of few samples. The detection model's ability to recognize defects of multiple types and small objects is simultaneously improved. Thirdly, the split federated learning (SFL) framework enables collaborative training of industrial surface defect detection models with a low resource cost. Our scheme improves model training efficiency and achieves high accuracy detection for small amounts of defect samples. Finally, the experimental results show that MAF with the aid of mixForm achieves 82.91 mAP on the NEU-DET dataset. Using MAF, the defect detection algorithm achieves at least 1.89 mAP improvement over using other attention mechanisms. The experiments also demonstrate that SFL achieves faster convergence and higher detection performance than traditional federated learning approaches.