Defect Detection of Aluminum Profiles based on Improved Feature Pyramids

Abstract

For the surface defects of aluminum profiles, there are problems of multi-scale, small object and irregular shape. This paper proposes a defects detection algorithm based on improved feature pyramid. This method compresses and saves the feature information extracted by the backbone networks, and calculates the similarity between deep and shallow features, so as to alleviate the phenomenon of loss of feature information and weakening of feature expression ability, thereby solving the problem of multi-scale and small object. At the same time, deformable convolution is introduced to enhance the feature extraction ability of the model and alleviate the detection problems caused by irregularly shaped defects. To verify the effectiveness of the proposed method, Faster R-CNN was used as the basic detection algorithm to conduct ablation experiments, and compared with the classical detection algorithm, the accuracy rate was as high as 72.8%. The experimental results show that the proposed method has a good performance on the task of aluminum profile defects detection, and is superior to the comparative detection algorithms.