The intelligentisation of coal mines is the only approach to the high‐quality development of the coal industry. Detection, identification and sorting of coal gangue is an important part of the intelligentisation of coal mines. Focusing on various problems in coal gangue detecting and recognising algorithms, such as limited receptive field, slow convergence rate and low accuracy of small particle recognition, this paper proposes a coal gangue detection and recognition algorithm based on deformable convolution YOLOv3 (DCN‐YOLOv3). To improve the accuracy of anchor frame positioning and enhance the diversity of the dataset, the deformed convolution YOLOv3 network model is established based on the detection algorithm YOLOv3, using deformable convolution, multiple k‐means clustering results average method and data enhancement technology as means. The model was trained through the self‐designed dataset, and the algorithm's correctness and accuracy for coal gangue recognition under different size and illumination conditions are verified. The test results showed that the algorithm effectively detects and recognises coal gangue, improves the accuracy and efficiency of detecting and recognising small‐size coal and gangue and improves environmental robustness. Furthermore, compared with the traditional recognition algorithm, the network convergence speed of this algorithm is significantly improved, the mAP is increased to 99.45%, and the maximum FLOPs value is reduced by 61.4%. Accordingly, this research is considered to be of certain theoretical value and technical reference for identifying coal gangue.

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.

The intelligentization of coal mines is the only approach to the high-quality development of the coal industry. Detection, identification, and sorting of coal gangue is an important part of the intelligentisation of coal mines. For the problems of traditional target detection algorithm model, such as high complexity, difficult training, and poor identification and positioning effect on coal and gangue under complex conditions, an identification and positioning method for coal gangue based on lightweight mixed domain attention was proposed. First, a mixed domain attention model was constructed through residual connection of spatial attention and efficient channel attention, and the MobileNetv3 network was optimized to improve its detection effect on coal and gangue under different conditions. Secondly, the optimized network was used to replace the feature extraction network in YOLOv4, and lightweighted using depthwise separable convolution to reduce model complexity and thereby obtain the ECSMv3_YOLOv4. Then the relationship between the actual position of coal and gangue on the conveyor belt was derived. Finally, after an experimental device was set up, a multi-condition coal and gangue dataset was built, the model was trained, as well as the identification and positioning results of the model under different sizes and light intensities were tested, and compared with those of other algorithms. Experiment results showed that the algorithm has the highest detection accuracy and efficiency, and can correctly identify and position coal and gangue under complex conditions with high confidence values. Compared with other algorithms, the mAP value of the proposed method is increased by 2.84% at most. The FLOPs was only 2,610,046, and the FPS of detection reached 28.8. Accordingly, this research is considered to be of certain theoretical value and technical reference for identification and positioning of coal gangue.

Accurate identification of coal and gangue is essential for clean and efficient use of coal. Existing target detection algorithms are ineffective in detecting small-target and overlapping gangue, and contain complex network structure and large parameter volume, which cannot meet the demand of...