An Adaptive Anchor Neural Network for Defect Detection in Aluminum Profiles Images

The quality of Aluminum Profiles is the most important evaluation criterion in industrial production. To perform the quality control of Aluminum Profiles, strict defect detection must be carried out. Traditional machine learning methods need to design hand-crafted features in advance. Deep learning methods need to preset anchor parameters according to all defects, which is inefficient and inaccurate. In this paper, we propose an adaptive anchor network with an attention-based refinement mechanism for defect detection. The network has learnable parameters to generate anchors adaptively. Meanwhile, to better represent the different defects, we design a refinement module with the channel and spatial attention mechanism and deformable convolution at the stage of feature extraction. Besides, we also use cascade detection architecture to retain more defect information. The proposed method gets the AP of 62.4 and AP(50) of 86.1 on an industrial dataset, which has AP of 12.8 and AP(50) of 17.8 improved to the conventional methods and outperforms several state-of-the-art methods.