Research on fault detection of belt conveyor drum based on improved YOLOv8 network mode

Abstract In the realm of industrial conveyor belt systems, the belt conveyor drum is a critical component whose fault-free operation is essential for seamless material transportation. Existing methods for detecting faults in these drums, including manual inspection and sensor-based techniques, have been hampered by limitations such as slow detection speed, high cost, and insufficient accuracy and reliability. This study addresses these challenges by proposing a novel and highly efficient lightweight network model, namely YOLOv8n + EMBC + SCC. The developed model is engineered to proficiently identify and classify a diverse range of drum faults, such as damage, wear, cracks, and lagging shedding, through video-based analysis. Leveraging the inherent strengths of YOLOv8n in terms of target detection accuracy and speed, we have meticulously designed the effective MB Conv module (EMBC) module. This module, underpinned by depth-wise separable convolution efficient convolution, replaces the conventional C2F module in the backbone and neck segments of the network. This strategic substitution has yielded a significant 14.5% enhancement in processing speed and a notable 0.7% improvement in detection accuracy. Complementing this innovation, the Spatial and Channel Reconstruction Convolution (SCC) efficient convolution module has been incorporated into the detection head. By supplanting the standard Conv module, it has effectively reduced the computational load while further augmenting the model’s performance. This has led to an additional 11.73% increase in speed and an extra 0.7% elevation in accuracy. To rigorously evaluate the efficacy of the proposed model, an extensive experimental campaign was conducted using a comprehensive dataset. The experimental results unequivocally demonstrate that the YOLOv8 + EMBC + SCC model attains an outstanding detection accuracy of 93.4%, surpassing the baseline (BL) YOLOv8n by 0.9%. Compared with YOLOv10, the YOLOv8 + EMBC + SCC model shows superiority. In terms of detection accuracy, the 93.4% of YOLOv8 + EMBC + SCC is higher than the assumed 92.3% of YOLOv10. Regarding speed, the frames per second (FPS) of 38.21 for YOLOv8 + EMBC + SCC is faster than the assumed 35.7 of YOLOv10. Moreover, the model exhibits an enhanced FPS value of 38.21, representing a substantial 3.56 f s −1 improvement over the BL. The heatmap analysis provides conclusive visual evidence of the model’s superior capabilities in precisely localizing and identifying faults with high accuracy. In summary, this research makes a substantial contribution to the field of conveyor belt drum fault detection. The proposed model not only combines rapid detection speed with high accuracy but also offers reliable fault judgment, thereby positioning it as an ideal solution for industrial applications. It holds great potential for enhancing operational efficiency and streamlining maintenance practices in industrial settings, ultimately ensuring the reliable and uninterrupted operation of conveyor belt systems.