Coal gangue detection and recognition method based on multiscale fusion lightweight network SMS-YOLOv3

Aiming at the problems of large memory footprint, low detection speed, and low detection accuracy for small and overlapping targets existing in the current coal gangue target detection algorithm, a real-time detection method for coal gangue based on a multiscale fusion lightweight network (SMS-YOLOv3) is proposed. Taking MobileNetv3 as a feature extraction network, in which all SE modules are replaced with SKNet, thus improving the ability of image feature extraction and making more effective use of parameters. A shallow detection scale is added to form a detection structure with the fusion of four scales to improve the detection accuracy of small targets. The spatial pyramid pooling is added after the backbone network to convert different feature maps into fixed feature maps, to improve the detection accuracy of the algorithm. CIoU bounding box regression loss and the K-means++ clustering anchorbox are used to improve the detection accuracy of targets. Experimental equipment was built, and coal gangue datasets of small size, large size, dim light, mutual concealment, and a large number of coal gangue under multiple conditions were constructed. Experiment results demonstrate the effective and fast detection of the proposed algorithm for small targets and overlapping targets of coal gangue accurately, with mAP reaching 98.97%. The algorithm has an mAP improvement of 0.37% and an fps increase of 119.04% compared with the original YOLOv3, with memory only 1/24 of the original.

Automated summary

A real-time detection method for coal gangue based on a multiscale fusion lightweight network (SMS-YOLOv3) is proposed to solve the problems of large memory footprint, low detection speed, and low detection accuracy for small and overlapping targets in the current coal gangue target detection algorithm. The proposed method uses MobileNetv3 as a feature extraction network and adds a shallow detection scale to improve the accuracy of small target detection. Experimental results show that the proposed algorithm achieves accurate and fast detection of small and overlapping targets of coal gangue with an mAP of 98.97%. The algorithm also demonstrates improvements in mAP and fps compared to the original YOLOv3, with a significantly reduced memory footprint.