Progressive refined redistribution pyramid network for defect detection in complex scenarios

Abstract

Due to the rapid development of manufacturing capabilities and the general improvement in requirements for product quality, the role of quality inspection in the industrial production process is becoming increasingly important. Unlike the case for natural objects, detailed information is particularly crucial in defect classification and localization, resulting in poor performance of general object detectors on complex defect detection tasks. Therefore, this paper proposes a progressively refined redistribution pyramid network for visual defect detection in complex images, in which three novel components are designed. (1) The aligned dense feature pyramid network (AD-FPN) refines scale differences and performs efficient alignment, alleviating feature misalignment in FPN-based methods. (2) The phase-wise feature redistribution module (PFRM) enhances the interaction between features across layers, where global information is reassigned in a semantically adaptive manner. (3) The adaptive feature purification module (AFPM) helps the network distinguish defects from complex backgrounds, and its update is directly supervised by an auxiliary branch to accelerate convergence. These ideas are all implemented based on YOLOv5. Extensive experiments on the Tianchi fabric dataset, the publicly available surface defect dataset NEU-DET, and the PCB defect dataset show that our method outperforms other state-of-the-art defect detection methods on most evaluation metrics. In addition, experimental results on the remote sensing dataset RSOD and pothole image dataset also demonstrate the strong generalization ability of our method in other complex scenarios.