Xu Guangpeng, Li Aijuan, Wang Xibo, Xu Chuanyan, Chen Jiaqi, Zheng Fei
School of Automotive Engineering, Shandong Jiaotong University, Jinan, 250357, China.
Research and Development Department, Shandong Wonderful Intelligent Technology Co., LTD, Jinan, 250101, China.
Sci Rep. 2024 Nov 28;14(1):29587. doi: 10.1038/s41598-024-80006-z.
Tire X-ray nondestructive testing before leaving the factory is crucial for driving safety. Given the complexity of tire structures and the diversity of defect types, traditional manual visual inspections and machine learning methods face significant challenges in terms of accuracy and efficiency. This study proposes an innovative tire X-ray image nondestructive testing technique based on the YOLOv5 model, incorporating several advanced technologies to enhance detection performance. Specifically, we introduce Dynamic Snake Convolution (DSConv), which adaptively focuses on slender and curved features within tires. Additionally, we have designed a C3 module based on DSConv, specifically targeting slender defects such as cord-overlap and cord-cracking. To improve the detection accuracy of small defects, we redesigned the neck network structure and introduced the Scale sequence feature fusion module (SSFF) and the Triple feature encoding module (TFE) to integrate multi-scale information from different network layers. Furthermore, we developed the Convolution Block Attention Module, integrated into the SSFF, which effectively reduces the interference of complex backgrounds and focuses on defect recognition. In the post-processing stage, we employed the Soft-NMS algorithm to optimize the confidence of candidate detection boxes, enhancing the accuracy of box selection. The experimental results show that compared to the YOLOv5 benchmark model, the algorithm proposed in this study achieved a 5.9 percentage point increase in mAP and a 5.7 percentage point increase in mAP, demonstrating superior detection accuracy compared to current mainstream object detection algorithms and effectively completing the nondestructive testing task of tire defects.
轮胎出厂前的X射线无损检测对行车安全至关重要。鉴于轮胎结构的复杂性和缺陷类型的多样性，传统的人工目视检查和机器学习方法在准确性和效率方面面临重大挑战。本研究提出了一种基于YOLOv5模型的创新性轮胎X射线图像无损检测技术，融合了多种先进技术以提升检测性能。具体而言，我们引入了动态蛇形卷积（DSConv），它能自适应地聚焦于轮胎内部的细长和弯曲特征。此外，我们基于DSConv设计了一个C3模块，专门针对诸如帘线重叠和帘线开裂等细长缺陷。为提高小缺陷的检测精度，我们重新设计了颈部网络结构，并引入了尺度序列特征融合模块（SSFF）和三重特征编码模块（TFE）来整合来自不同网络层的多尺度信息。此外，我们开发了卷积块注意力模块，并将其集成到SSFF中，有效减少复杂背景的干扰并专注于缺陷识别。在后期处理阶段，我们采用Soft-NMS算法来优化候选检测框的置信度，提高框选择的准确性。实验结果表明，与YOLOv5基准模型相比，本研究提出的算法在平均精度均值（mAP）上提高了5.9个百分点，在mAP上提高了5.7个百分点，与当前主流目标检测算法相比具有更高的检测精度，有效完成了轮胎缺陷的无损检测任务。
