In view of the poor recognition performance of the existing foreign object detection models for coal mine conveyor belts in the complex underground environment, they are prone to false detection and missed detection of slender foreign objects and small target foreign objects. Moreover, the models are large in size, difficult to deploy on edge devices, and the detection methods are slow, have numerous parameters, and involve a large amount of computation. A foreign object detection algorithm for downhole conveyor belts based on the improved YOLOv11 is proposed. Firstly, the ADown downsampling module is incorporated to enhance the detection performance for small defects and reduce the number of parameters. Secondly, the SegNext attention mechanism is integrated to enhance the model's performance in image segmentation. Thirdly, the C3k2 module is optimized by integrating the Light-weight Context Guided mechanism from the CGNet framework. This enhancement significantly boosts the model's deployment flexibility and detection speed in complex underground environments. Finally, the lightweight detection head, LSCD, is utilized to enhance the model's capability in handling multi-scale features. This is achieved through the implementation of shared convolutional layers, which effectively reduce the computational load and parameter size. Moreover, the effectiveness of the enhanced model is further validated through extensive experimental comparisons. The experimental results show that, compared with the original model, the improved model has a 1.5% increase in mAP, a 1.2% increase in Precision, a 2% increase in Recall, a 28% reduction in the number of parameters, a 33% decrease in computational load, and a 29% reduction in the model storage size. This indicates the effectiveness of this method in the detection of foreign objects in coal mine conveyor belts. It has important reference significance for the real-time detection of foreign objects in the conveyor belts of underground coal mines.
Ling et al. (Thu,) studied this question.