# Tire Defect Detection via 3D Laser Scanning Technology
## 1 Introduction  
Automatic detection technology plays an important role in many fields of industrial quality inspection, such as those for steel [1], solar wafers [2], airplanes [3], and fabrics [4, 5, 6]. Compared with human inspection, automatic defect detection has high accuracy and efficiency, which significantly lowers the risk of human intervention in hazardous environments. With the fast advancements in computer vision in the past two decades, an increasing number of vision-based quality inspection technologies has been applied in many real-world applications. Thanks to the learning capacity and generalization ability of advanced detection methods, such as convolutional neural networks (CNNs [7] and transformers [8, 9]), training models with an insufficient number of training data in various scenarios of the industrial field have become possible. With the demand for intelligent manufacturing, defect detection techniques have become a rising topic in the tire industry for quality inspection. Because of external factors of the manufacturing process and the mechanical environment, tire products inevitably suffer from various types of defects. Among these defects, bulges are a major concern in actual production lines, especially for tires used in engineering vehicles. The causes of this type of defect can be summarized into two types: (1) Rubber particles or other impuritiesare mixed between the interior layers of the tire, leading to a clear bulge on the tire surface (impurity bulges). (2) External air or high-temperature chemical components are not fully eliminated during production (bubbles). With the advances in mechanical devices and manufacturing techniques, the formation of impurity bulges has been significantly reduced in tire industries. However, bubble defects are still a major threat to the quality and reliability of tire products, which are dangerous to driving conditions. Existing research on tire defect detection treats different bulges as a general type of defect and mainly focuses on impurity bulges that have distinctive features. However, bubble defects present a visual quality very similar to those of surrounding areas, making them extremely difficult to detect in the manufacturing process. Because of the intrinsic limitations of detecting defect types with very low visibility, the inspection of bubble defects on tire surfaces remains a challenging task in both academic and industrial fields. A common way to detect bubbles is via human-eye inspection in real production lines. Such a process requires experienced workers with the help of strong light sources to reveal possible irregular surface areas, which is time-consuming and has low accuracy (see Figure 1a). Meanwhile, other typical types of tire defects, such as exposed cords or cracking, are normally detected using X-ray inspection techniques [10; 11; 12], where radiographic images are captured to detect defects with clear sharp edges or color differences. Unfortunately, considering the extremely low visual quality of bubble defects, X-ray scans are insensitive to this type of defect, as shown in Figure 1. Another solution is the use of laser speckles techniques, but this method is not very common in industrial defect detection. During processing, an inspected tire should be placed in a vacuum space where the inner air pressure could extend the size of bubble defects. By comparing speckle patterns before and after processing, locating smaller bulges on tire surfaces is easier. However, this solution requires complicated procedures and is inefficient for real-time inspection. Figure 1: (**a**) Tire bubble defects, a major defect type usually inspected by experienced workers using additional light sources. We show that this type of defect is difficult to detect in X-ray (**b**) or RGB (**c**) images because of its low visual quality. (**d**) shows the data used in our method, presented as 3D point clouds and then converted into rendered 2D images (**e**) for subsequent detection. Red frames indicate the locations of defects.