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 difficult problem of coal gangue image segmentation in complex backgrounds, this paper proposes an image segmentation method based on the edge detection theory of the star algorithm. The pixel matrix is extracted one by one in the X and Y directions of the coal gangue image, and the central pixel of the matrix satisfying the monotonic change condition is assigned as 0. They are mapped to single-value images with equal size in turn, to realize the detection of coal and gangue edges in the images. The response strategy of adjusting matrix length n and assignment factor β in real-time by using the feedback result of the illuminance meter in changing illumination environment is given. Combining the edge detection method of the star algorithm with the morphological method, the fine segmentation of the coal gangue image is completed. The segmentation results are based on the segmentation results obtained by the AI algorithm, and the error rates of the pixel area and centroid coordinates of coal gangue are within 0.29%. This study provides a novel, precise and efficient solution to the problem of image edge detection and segmentation in complex backgrounds.

In the process of pre-discharging gangue via the dual-energy X-ray coal and gangue sorting device, the pseudo-medium coal in the raw coal will be identified as gangue and then discarded. Clean coal can be recovered and resource waste can be reduced via the crushing and re-separation of pseudo-medium coal, which is the critical step in the treatment procedure of a coal preparation plant. By virtue of dual-view visible-light imaging, an image identification method was proposed to recover the pseudo-medium coal identified as gangue by dual-energy X-ray. The pseudo-medium coal was determined based on the differences in coal and gangue surface organization and reflective properties. Linear dispersion was used to evaluate the distinguishability of features, and the elements were sorted. The recognition effect of the classifier model on pseudo-medium coal and gangue under different feature combinations was experimentally observed. The results unraveled that the identification rate of mixed samples of pseudo-medium coal and gangue that were selected from Dayugou Coal Mine in Zhengzhou City, Henan Province, could reach 95% using the method herein. The invention can effectively recover pseudo-medium coal from the waste rock pre-discharged by the dual-energy X-ray sorting device, realizing the multi-classification of raw coal.

Coal and gangue separation technology based on X-ray has broad development prospects due to its low energy consumption and pollution-free environment, which is also the key part in the green and intelligent mines. Aiming at the low recognition accuracy for coal and gangue with the particle size of 5–15 mm for the dual-energy X-ray coal and gangue separation technology, the coal and gangue recognition method based on the combination of X-ray transmission and diffraction principle was proposed. The dual-energy X-ray images based on the X-ray transmission principle were collected for coal and gangue with particle size larger than 15 mm, and Rc, Glc, Gl, Ra were extracted as the recognition features. EDXRD patterns based on the X-ray diffraction principle were collected for coal and gangue with particle size less than 15 mm, and the characteristic diffraction peaks were extracted as the recognition features. Then, the PSO-SVM model was established for coal and gangue recognition. The test results show that the proposed method can broaden the particle size range for dry coal preparation based on X-ray, and the recognition accuracy of coal and gangue with particle size less than 15 mm is 98%, which is 16.7% higher than that of the method based on the X-ray transmission principle alone. The comprehensive recognition accuracy of coal and gangue with particle size of 5–100 mm reached 97.5%. Consequently, this paper provides a new technical approach for coal and gangue identification.

It is one of the most critical technologies for unmanned electric locomotives to detect the obstacles in front of their operation quickly and accurately, which is of great significance for the safe operation of electric locomotives. Aiming at the problems of current computer vision detection methods, such as error warning, low detection accuracy, and slow detection speed, an obstacle intelligent detection method for unmanned electric locomotives based on an improved YOLOv3 (YOLOv3-4L) algorithm is proposed. The obstacle image data set of the electric locomotive running area is constructed to provide a testing environment for various obstacle detection algorithms. In the network structure, the darknet-53 feature extraction network is simplified, and the four-scale detection structure is formed by adding the shallow layer detection scale to the detection layer, which can improve the detection speed and accuracy of the algorithm for obstacles in front of the locomotive. Distance intersection over union loss function and Focal loss function are adopted to redesign the loss function of the target detector to further improve the detection accuracy of the algorithm. Traditional computer vision techniques such as perspective transformation, sliding window, and least square cubic polynomial are used to detect the track lines. By finding the area where the track was located and extending a certain distance to the outside of the track, the dangerous area of electric locomotive running is obtained. The improved YOLOv3 algorithm is utilized to detect obstacles, and only the types and positions of obstacles coincident with dangerous areas are output. The experimental results show that the traditional computer vision techniques such as perspective transformation, sliding window, and least square cubic polynomial can detect not only straight track but also curved track, which makes up for the shortcomings of the Hough transforms in detecting curved tracks. Compared with the original YOLOv3 algorithm, the YOLOv3-4L algorithm improves the mean average precision by 5.1%, and the detection speed increases by 7 fps. YOLOv3-4L detection model has high detection accuracy and speed, which can meet the actual working conditions and provide technical reference for unmanned driving of electric locomotives in underground coal mines.