Multi-Scale Feature Fusion to Improve Faster RCNN for Aluminum Surface Defect Recognition
Aiming at the problem that it is difficult to automatically detect the surface defects of aluminum profiles with various types and complex defect features,an aluminum surface defect detection model based on improved Faster RCNN is designed in this paper.In this model,ROI Pooling is replaced by ROI Align to reduce the error of defect localization.Then Darknet-53 combined with FPN is used as the backbone net-work to improve the extraction ability of small defects.Finally,Cosine annealing is used to optimize the learning rate in model training to further accelerate model convergence and improve model detection accu-racy.In the final test,the average recognition accuracy of the method for aluminum surface defect can reach to 96.5%,and the detection time for a single image is only 0.373 s.
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