Coal mine conveyor belt foreign objects detection is critical in conveyor belt transportation of coal. Aiming at the problems that the existing coal mine conveyor belt foreign objects detection model has a large number of parameters, occupies more computer resources, and detects fewer types of foreign objects, the original YOLOv13 object detection algorithm is optimized to achieve lightweight design and high precision. Therefore, a sophisticated lightweight YOLO network named LRNet is proposed based on the original YOLOv13, which is tailored for foreign objects detection on coal mine conveyor belts. First, lightweight ShuffleNetv2 is used as the backbone network for YOLOv13 to reduce computational cost and the number of parameters, and to improve the network parallelism. Second, the Bidirectional Feature Pyramid Network (BIFPN) is used as a feature fusion network to effectively fuse global deep and shallow key detail information. Finally, the Coordinate Attention (CA) mechanism is added to enhance the extraction capability of key features and strengthen the foreign objects target attention to improve the network model detection accuracy. The experimental results show that the average detection accuracy of LRNet reaches 91.0%, the number of parameters is 3.6 M. The proposed method can quickly and accurately detect foreign objects in coal mine conveyor belts with less computational resources, and at the same time, it shows strong adaptability and anti-interference ability, which reflects the effectiveness and advancedness of the LRNet model.
Xu et al. (Mon,) studied this question.