Combined Anomaly Aware Weakly Supervised Lightweight Model for Surface Defect Inspection

Surface defect inspection plays a vital role in the industrial production process. Many detection methods based on deep learning have been gradually applied because of their better generalization performance. However, achieving accurate annotations for training deep learning models remains a challenge due to the difficult definition of defect boundaries and the high cost of manual annotation work. Meanwhile, the detection performance of the current deep-learning methods still cannot meet the needs of industrial applications. To address these issues, this article proposes a combined anomaly aware weakly supervised lightweight model that only requires image-level labels for training and outputs defect localization. In the framework, we first design a lightweight backbone to obtain feature maps. Then, we propose a novel weakly supervised localization (WSL) method to obtain anomaly responses and use them as prior knowledge of the downstream network. Finally, the final defect detection result is obtained through the work of the designed downstream fine inspection network. In addition, we will employ multiple supervisions throughout the framework for full data use. The results of the evaluation on four real-world defect datasets demonstrate that the proposed method is superior and more generalized than state-of-the-art WSL methods and defect detection methods on average precision.