Lightweight Target Detection for Coal and Gangue Based on Improved Yolov5s

Abstract

The detection of coal and gangue is an essential part of intelligent sorting. A lightweight coal and gangue detection algorithm based on You Only Look Once version 5s (Yolov5s) is proposed for the current coal and gangue target detection algorithm with the low accuracy of small target detection, high model complexity, and sizeable computational memory consumption. Firstly, we build a new convolutional block based on the Funnel Rectified Linear Unit (FReLU) activation function and apply it to the original Yolov5s network so that the model adaptively captures local contextual information of the image. Secondly, the neck of the original network is redesigned to improve the detection accuracy of small samples by adding a small target detection head to achieve multi-scale feature fusion. Next, some of the standard convolution modules in the original network are replaced with Depthwise Convolution (DWC) and Ghost Shuffle Convolution (GSC) modules to build a lightweight feature extraction network while ensuring the model detection accuracy. Finally, an efficient channel attention (ECA) module is embedded in the backbone of the lightweight network to facilitate accurate localization of the prediction region by improving the information interaction of the model with the channel features. In addition, the importance of each component is fully demonstrated by ablation experiments and visualization analysis comparison experiments. The experimental results show that the mean average precision (mAP) and the model size of our proposed model reach 0.985 and 4.9 M, respectively. The mAP is improved by 0.6%, and the number of parameters is reduced by 72.76% compared with the original Yolov5s network. The improved algorithm has higher localization and recognition accuracy while significantly reducing the number of floating-point calculations and of parameters, reducing the dependence on hardware, and providing a specific reference basis for deploying automated underground gangue sorting.