Existing methods for detecting damage in conveyor belts are affected by multiple factors such as complex underground working environments, uneven illumination and high dust concentration. Furthermore, these methods exhibit low detection efficiency, insufficient accuracy and both false and missed detections. Therefore, in this study, an improved You Only Look Once version 8 small (YOLOv8s) damage detection algorithm for mining conveyor belts was developed, called GCW-YOLO. The lightweight Ghost module was combined with the C2f module to form the C2fGhost structure, enabling a lightweight model by generating more feature maps to enhance network performance, which significantly reduces the number of parameters. Therefore, the model demonstrates high performance while reducing the number of parameters and computational complexity, while simultaneously accelerating detection speed. A coordinate attention (CA) mechanism was embedded into the backbone network to enhance feature extraction capability by adaptively assigning weights to features of different channels, highlighting important features while suppressing irrelevant ones, and improving feature representation in complex scenarios. The wise intersection over union version 3 (WIoUv3) was used as the bounding box loss function instead of the original loss function to improve model convergence and regression accuracy. The loss calculation was adjusted to help the model focus on important targets or regions by assigning different weights to various regions or target categories. Experimental results indicated that, compared with the original YOLOv8s algorithm, the improved algorithm achieved an average detection accuracy of 89. 7% for the types of damage investigated, representing an improvement of 4. 9%. Furthermore, compared with the original algorithm model, the number of model parameters was reduced by 16%, the computational load decreased by 3. 7 giga floating-point operations per second (GFLOPS) and the average detection speed reached 55 frames per second (FPS). The improved algorithm ensures accurate detection and improved detection speed of conveyor belt damage in coal mines, demonstrating significant practical value.
Wang et al. (Sun,) studied this question.