Hui Sun, Qiao Zhou, Zhihui Hu, Yu Tian, Yueying Wang, and Qiang Le
Conveyor belt, damage detection, YOLOv8, RepVit network, attention mechanism, lightweight head
Belt conveyors are one of the key transportation equipment for continuously conveying bulk materials in bulk cargo terminals, and their complex operational environments pose challenges for real-time visual detection of conveyor belt damage. In light of the severe degradation of image feature information concerning longitudinal tears on conveyor belts in complex environments, which increases the difficulty of extracting damage features, a conveyor belt damage detection method based on the YOLOv8 visual detection model is proposed. Firstly, by replacing CSPDarknet53 with the new lightweight network RepVit as the backbone feature extraction network and optimising MobileNet-V3-L based on the architecture of Vision Transformers, the enhancement of image feature extraction capability under complex backgrounds is achieved. Subsequently, a deformable attention mechanism (DAT) is integrated at the back end of the RepVit network to focus the network more on the damage target detection part, adaptively adjusting the receptive fields to better handle variations in damage feature shapes and sizes, enabling rapid and accurate detection of surface damage on conveyor belts. Furthermore, lightweight processing is applied to the YOLOv8 detection head, altering the convolution method to decrease network computational complexity and parameters, thereby enhancing network detection accuracy. The experimental results show that the proposed method is highly reliable, effective, and performs in real time. The model achieves a mean average precision of 93.70% on a dataset of complex operating conditions, demonstrating a significant improvement in detection performance compared to other networks. The frames per second is 25.8, meeting the requirements for real-time detection of conveyor belt damage.
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