Deep LBP-Enriched Real-time Segmentation for Hair Defect Detection

Abstract

Hair defects are common in the industrial production of medical syringes, posing a significant risk to product quality and efficacy. Detecting these defects in real-time is crucial for ensuring high-quality production.However, existing Deep semantic segmentation (DSS) methods, which generally have numerous network parameters,face significant challenges in real-time hair defect detection due to hair's unique characteristics, including its irregular and thin structure. Moreover, potential hair overlapping with the syringe further complicates the detection process. In this case, conventional DSS methods are hard to explore the accurate low-level visuospatial information that is critical for detecting hair defects. Considering the wide applicability and effectiveness of the handcrafted features, such as Local Binary Pattern (LBP) and Sobel operators, in defect detection, we argue that these features designed by skillful experts may encode rich prior knowledge about defects and may improve the performance of DSS models for hair defects on syringes.Inspired by this idea, this study proposes a Deep LBP-Enriched Real-time Segmentation (DLERS) method for hair defects detection, which maintains a lightweight network structure and leverages the LBP encoding mechanism to facilitate the effective transfer of domain prior knowledge.Besides, to alleviate the influence of the hair-like noise and fragmentary edges, we propose employing a joint loss function that combines the Dice loss, BCE loss, and Edge loss to train our network. To evaluate the performance of DLERS, we conduct experiments on one real-world syringe dataset.The competitive results (e.g., 85.36% MIoU and 149.1 FPS) prove the effectiveness of our method.