# Tire Defect Detection via 3D Laser Scanning Technology
## 1 Introduction
Considering the difficulty and inefficiency of detecting bubble defects in tire defect detection, we aimed to search for a more suitable solution that can capture these defects' characteristics for efficient inspection. Despite their different natures, bulge-like defects can be defined as irregular areas on the relatively flat surface of a tire product, presenting certain features in terms of depth distribution. Therefore, we extracted three-dimensional (3D) surface data for the detection of bubble defects and established a new dataset of scanned tire surfaces using laser scanners, which were then presented as 3D point clouds. Thanks to the development of 3D acquisition techniques, today's scanners can provide accurate results with less than 0.1 mm of scanning error, providing accurate data for detecting smaller characteristics. We show in Figure 1d that very small bulges can be reflected as points arranged in a curved scanning line even though they are hardly visible in the two-dimensional (2D) domain. By analyzing the point distribution in the 3D domain, such defects are much easier to detect at very small scales. We propose a new tire defect detection pipeline unlike previous methods by combining both 3D and 2D information for analyzing and detecting bulge-like tire defects. Because of the irregularity and sparse nature of 3D point cloud data, the raw scanned surface may contain undesired scanning noise and regular textures on the tire surface. Therefore, locating accurate positions of the target defects from the raw 3D scans is intrinsically difficult (see Figure 1d). Moreover, we propose the combination of the captured 3D information and 2D detection pipelines to take advantage of the powerful learning capacity of existing detection models. Our method consists of two main stages. In the first stage, we extract 3D information from the raw point cloud data and estimate the surface curvature of each point in its local region using a normal-based rendering strategy. A rendered 2D feature map (see Figure 1e) is obtained for the subsequent detection model, which contains pixel-wise surface orientation for detecting irregular bulges on the scanned surface. Second, given the obtained feature map, we design a transformer-based detection network to predict possible areas of the target defects. The qualitative and quantitative results demonstrate that our method outperforms existing tire-defect-detection methods in terms of detection accuracy. Our contribution can be summarized as follows:  
* Our method is the first work focusing on the detection of low-visibility tire defects and establishes a new 3D dataset consisting of scanned tire surfaces from laser scanners. * We propose a new detection pipeline for tire defects by combining 3D surface data and a 2D detection model to effectively detect challenging tire defects. * We design a normal-based rendering strategy to extract surface orientation from scanned raw point clouds, which provides effective 2D depth information for the subsequent detection process. * We apply a novel transformer-based detection model for detecting accurate areas of target defects.