Research on Surface Defect Detection of Rare-Earth Magnetic Materials Based on Improved SSD

Abstract

In order to overcome the limitation of manual visual inspection of surface defects of rare-earth magnetic materials and increase production efficiency of traditional rare-earth enterprises, a detection method based on improved SSD (Single Shot Detector) is proposed. The SSD model is improved from two aspects for better performance in the detection of small defects. First of all, the multiscale receptive field module is embedded into the backbone network of the algorithm to improve the feature extraction ability of the model. Secondly, the interlayer feature fusion strategy of bidirectional feature pyramid in PANet (path aggregation network) is integrated into the model. In order to enhance the detection ability of the model, the high-level semantic information is strengthened by an efficient channel attention mechanism. The detection speed of the improved SSD algorithm is 55FPS, and the mAP (mean Average Precision) is up to 83.65%, which is 3.41% higher than of the original SSD algorithm, and the ability to identify small defects is significantly improved.