An Integrated Deep Learning Approach for Crack Detection and Localization in Tyre Images

The production of tyres plays a crucial role in the expansion of the automotive sector. The journey from the first pneumatic tyre to the development of high-performance tyres has been a gradual process for the auto industry. Despite the extensive integration of artificial intelligence in manufacturing and production, the identification of cracks and defects in tyres still heavily relies on human inspection. This manual process is time-consuming and prone to inaccuracies, adversely affecting overall output. This study addresses these challenges by employing ResNet50 for the classification of normal and cracked tyres, and the YOLO (You Only Look Once) object detection algorithm for crack detection and localization. The utilization of ResNet50 and YOLO in this study offers a transformative solution to the challenges plaguing the tyre production sector. By harnessing ResNet50 for the classification of normal and cracked tyres, and integrating YOLO for precise crack detection and localization, this research significantly enhances the efficiency and accuracy of tyre inspection processes. With ResNet50 achieving an impressive average accuracy of 92.65% in tyre image classification and YOLOv5 demonstrating a remarkable mean average precision (mAP) of 94.95%, this composition not only streamlines operations but also elevates quality control standards. This technological advancement promises to reduce manual labor, mitigate inaccuracies, and ultimately bolster the automotive industry's overall output and reliability.