IDD-Net: Industrial defect detection method based on Deep-Learning

Abstract

Detecting defects in industrial products is one of the most widespread applications of industrial automation. Various product defects, large similarities, and drastic changes in scale in industrial scenarios pose challenges to existing industrial inspection networks. This paper proposes a deep learning-based industrial defect detection method (IDD-Net) to address the above challenges. Specifically, IDD-Net has three distinct features. First, for the defects of diversity and similarity (rolled-in_scale, crazing in steel defects), IDD-Net designed a novel local–global backbone feature network (LGB-Net). Second, IDD-Net proposes a novel Three-Layer Feature Aggregation network (TFLA-Net) to solve the problem of drastic scale changes. TFLA-Net adopts a novel three-layer descending method to aggregate semantic and fine-grained features effectively. At the same time, the dense connection of adjacent nodes of TFLA-Net ensures the efficient fusion of features of different scales in the network. In particular, this paper proposes a novel IoU loss (Defect-IoU loss) for the problem of object loss deviation at different scales. The novelty of Defect-IoU Loss is that the loss value is scaled by the difference in the area of different scale objects, which is more conducive to the balance of multi-scale object loss. The experimental results show that the calculation amount of IDD-Net is only 24.9 Gflops, and the mAP@.5 of 79.66%, 99.5%, and 95.9% in the steel defect, aluminium defect, and PCB defect datasets were respectively obtained, surpassing all comparison models. In addition, the test in the actual industrial scene also demonstrates the feasibility of the application of IDD-Net.