Graduate Institute of Vehicle Engineering, National Changhua University of Education, No.1, Jin-De Road, Changhua City, 50007, Taiwan.
Sci Rep. 2023 May 17;13(1):8027. doi: 10.1038/s41598-023-35227-z.
Driving can understand the importance of tire tread depth and air pressure, but most people are unaware of the safety risks of tire oxidation. Drivers must maintain vehicle tire quality to ensure performance, efficiency, and safety. In this study, a deep learning tire defect detection method was designed. This paper improves the traditional ShuffleNet and proposes an improved ShuffleNet method for tire image detection. The research results are compared with the five methods of GoogLeNet, traditional ShuffleNet, VGGNet, ResNet and improved ShuffleNet through tire database verification. The experiment found that the detection rate of tire debris defects was 94.7%. Tire defects can be effectively detected, which proves the robustness and effectiveness of the improved ShuffleNet, enabling drivers and tire manufacturers to save labor costs and greatly reduce tire defect detection time.
驾驶人员都知道轮胎胎面深度和气压的重要性，但大多数人都没有意识到轮胎氧化的安全隐患。驾驶员必须保持车辆轮胎的质量，以确保性能、效率和安全。在这项研究中，设计了一种深度学习轮胎缺陷检测方法。本文改进了传统的 ShuffleNet，并提出了一种用于轮胎图像检测的改进 ShuffleNet 方法。通过轮胎数据库验证，将研究结果与 GoogLeNet、传统 ShuffleNet、VGGNet、ResNet 和改进 ShuffleNet 这五种方法进行了比较。实验发现，轮胎碎屑缺陷的检测率为 94.7%。可以有效检测轮胎缺陷，证明了改进的 ShuffleNet 的健壮性和有效性，使驾驶员和轮胎制造商能够节省劳动力成本，并大大减少轮胎缺陷检测时间。
