CADN: A weakly supervised learning-based category-aware object detection network for surface defect detection. (January 2021)

• Record Type: Journal Article
• Authors: Zhang, Jiabin; Su, Hu; Zou, Wei; Gong, Xinyi; Zhang, Zhengtao; Shen, Fei
• Is Part Of: Pattern recognition. Volume 109(2021)
• Publication Date: 2021-01
• Subjects: Weakly supervised learning -- Automated surface inspection -- Defect detection -- Knowledge distillation; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107571
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A novel Category-aware Conv-Pooling module is proposed, which explores weak image tag annotation to extract spatial information. Knowledge distillation strategy is adopted to force the feature of a student CADN to mimic that of a teacher CADN, leading to accuracy improvement. As verified, weakly supervised defect detection is achieved and competitive results are obtained by using the proposed CADN method. In CADN, human labeling effort, accuracy and speed are simultaneously considered, making the method practical in industrial applications. Abstract: Large-scale data with human annotations is of crucial importance for training deep convolutional neural network (DCNN) to ensure stable and reliable performance. However, accurate annotations, such as bounding box and pixel-level annotations, demand expensive labeling efforts, which has prevented wide application of DCNN in industries. Focusing on the problem of surface defect detection, this paper proposes a weakly supervised learning method named Category-Aware object Detection network (CADN) to tackle the dilemma. CADN is trained with image tag annotations only and performs image classification and defect localization simultaneously. The weakly supervised learning is achieved by extracting category-aware spatial information in a classification pipeline. CADN could be equipped with either a lighter or a larger backbone network as the feature extractor resulting in better real-time performance or higher accuracy. To address the two conflicting objectives simultaneously, both of which are significant concerns in industrial applications, knowledge distillation strategy is adopted to force the learned features of a lighter CADN to mimic that of a larger CADN. Accordingly, the accuracy of the lighter CADN is improved while high real-time performance is maintained. The proposed approach is verified on our own defect dataset as well as on an open-source defect dataset. As demonstrated, satisfied performance is achieved by the proposed method, which could meet industrial requirements completely. Meanwhile, the method minimizes human efforts involved in image labelling, thus promoting the applications of DCNN in industries.