PDD-Net: A Precise Defect Detection Network Based on Point Set Representation

Abstract

Defect detection has been widely studied in computer vision and used in industrial production. However, most existing methods for defect detection mainly suffer three drawbacks: i) Low-contrast problem between defects and background. ii) Large scale changes in defects size. iii) Extreme imbalance problem between defects and background classes during training. To address these issues, we propose a novel anchor-free defect detection network named PDD-Net. Specifically, a global-context FPN (GC-FPN) is designed to capture long-range dependency between defects and background. Simultaneously, to enhance feature extraction of defects at different scales, a receptive field pyramid block (RFPB) is proposed to provide various receptive field sizes. Furthermore, an equipped adaptive positive and negative samples allocation (APNSA) mechanism is built with statistical characteristics of defects, thus can select training samples automatically. We conduct experiments on MPSD dataset, DAGM2007 dataset, and NEU-DET dataset. Extensive experimental results on the three challenging datasets show that our PDD-Net achieves superior detection accuracy over the state-of-the-art methods.