DEL_YOLO: A Lightweight Coal-Gangue Detection Model for Limited Equipment

摘要

The gangue mixed in raw coal has small feature differences from coal, in order to solve the existing gangue recognition, methods generally have slow detection speed and are difficult to deploy at the edge end of the problem, a lightweight gangue target detection algorithm is proposed to enhance the research for the field of coal mining. Firstly, a lightweight EfficientViT module is the backbone of the network; secondly is the introduction of the DRBNCSPELAN4 module, which can better capture target information at different scales; finally, the lightweight shared convolutional detection head Detect_LSCD is reconstructed in order to further reduce the model size and improve the detection speed for coal and gangue. The experimental results indicate that in the model compared with the original algorithm, mAP@50–95 is improved by 1.2%, model weight size, the number of parameters, and floating point operations are reduced by 52.34%, 55.35%, and 50.35%, respectively, and inference speed is accelerated by 20.87% on a Raspberry Pi 4B device. In the field of coal gangue sorting, the algorithm not only has high-precision, real-time detection performance, but also achieves significant results in the lightweight model, making it more suitable for deployment on edge equipment to meet the requirements of controlling the robotic arm sorting gangue.