ACA-Net: An Adaptive Convolution and Anchor Network for Metallic Surface Defect Detection

• Published in: Applied sciences Vol. 12; no. 16; p. 8070
• Main Authors: Chen, Faquan, Deng, Miaolei, Gao, Hui, Yang, Xiaoya, Zhang, Dexian
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-08-2022
• Subjects: Algorithms; anchor box; Classification; computer vision; Convolution; convolutional neural network; Datasets; Deep learning; feature fusion; Feature maps; Industrial production; Industrial products; Localization; Methods; Neural networks; Performance evaluation; Sensors; Success; Support vector machines; surface defect detection
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12168070

Metallic surface defect detection is critical to ensure the quality of industrial products. Recently, human-advanced surface defect detection algorithms have been proposed. Most of these algorithms rely on convolutional neural networks (CNN) and an anchoring scheme. However, a convolution unit only samples the input feature maps at fixed shapes and locations. Similarly, a set of anchors are uniformly predefined with fixed scales and shapes, which increases the difficulties of bounding box regression. Therefore, we propose an adaptive convolution and anchor network for metallic surface defect detection, named ACA-Net. Specifically, an adaptive convolution and anchor (ACA) module is proposed, which mainly consists of adaptive convolution and an adaptive anchor. Firstly, an adaptive convolution module (ACM) is designed, which adaptively determines the location and shape of each convolution unit. In addition, a multi-scale feature adaptive fusion (MFAF) is proposed, which is used in ACM to extract and integrate multi-scale features. Then, an adaptive anchor module (AAM) is proposed to yield more suitable anchor boxes by adaptively adjusting shapes. Extensive experiments on NEU-DET dataset and GC10 dataset validate the performance of the proposed approach. ACA-Net achieves 1.8% on NEU-DET dataset higher Average Precision (AP) than GA-RetinaNet. Furthermore, the proposed ACA module is also adopted in GA-Faster R-CNN, improving the AP by 1.2% on NEU-DET dataset.