Tire Defect Detection via 3D Laser Scanning Technology

Abstract

Tire defect detection, as an important application of automatic inspection techniques in the industrial field, remains a challenging task because of the diversity and complexity of defect types. Existing research mainly relies on X-ray images for the inspection of defects with clear characteristics. However, in actual production lines, the major threat to tire products comes from defects of low visual quality and ambiguous shape structures. Among them, bubbles, composing a major type of bulge-like defects, commonly exist yet are intrinsically difficult to detect in the manufacturing process. In this paper, we focused on the detection of more challenging defect types with low visibility on tire products. Unlike existing approaches, our method used laser scanning technology to establish a new three-dimensional (3D) dataset containing tire surface scans, which leads to a new detection framework for tire defects based on 3D point cloud analysis. Our method combined a novel 3D rendering strategy with the learning capacity of two-dimensional (2D) detection models. First, we extracted accurate depth distribution from raw point cloud data and converted it into a rendered 2D feature map to capture pixel-wise information about local surface orientation. Then, we applied a transformer-based detection pipeline to the rendered 2D images. Our method marks the first work on tire defect detection using 3D data and can effectively detect challenging defect types in X-ray-based methods. Extensive experimental results demonstrate that our method outperforms state-of-the-art approaches on 3D datasets in terms of detecting tire bubble defects according to six evaluation metrics. Specifically, our method achieved 35.6, 40.9, and 69.1 mAP on three proposed datasets, outperforming others based on bounding boxes or query vectors.