The belt conveyor is the main equipment for underground coal transportation. Its coal flow is mixed with large coal, gangue, anchor rods, wooden strips, and other foreign objects, which easily causes failure of the conveyor belt, such as scratching, tearing, and even broken belts. Aiming at the problem that it was difficult to accurately identify the foreign objects of underground belt conveyors due to the influence of fog, high-speed operation, and obscuration, the coal mine belt conveyor foreign object recognition method of improved YOLOv5 algorithm with defogging and deblurring was proposed. In order to improve the clarity of the monitoring video of the belt conveyor, the dark channel priori defogging algorithm is applied to reduce the impact of fog on the clarity of the monitoring video, and the image is sharpened by user-defined convolution method to reduce the blurring effect on the image in high-speed operation condition. In order to improve the precision of foreign object identification, the convolution block attention module is used to improve the feature expression ability of the foreign object in the complex background. Through adaptive spatial feature fusion, the multi-layer feature information of the foreign object image is more fully fused so as to achieve the goal of accurate recognition of foreign objects. In order to verify the recognition effect of the improved YOLOv5 algorithm, a comparative test is conducted with self-built data set and a public data set. The results show that the performance of the improved YOLOv5 algorithm is better than SSD, YOLOv3, and YOLOv5. The belt conveyor monitoring video of resolution for 1920 × 1080 in Huangling Coal Mine is used for identification verification, the recognition accuracy can reach 95.09%, and the recognition frame rate is 56.50 FPS. The improved YOLOv5 algorithm can provide a reference for the accurate recognition of targets in a complex underground environment.
After a coal mine disaster, the coal mine roadway often forms an unknown nonstructural environment. Once the normal communication of the roadway is destroyed, the rescue operation of the coal mine rescue robot could be hindered. To restore the emergency wireless communication system in the unstructured environment of the coal mine thus becomes an important prerequisite for the rescue work for the coal mine rescue robot. This paper studies the characteristics of the nonstructural environment and sets up its model. Based on the Maxwell equation and the mechanism of radio electromagnetic wave propagation, this paper researches the electromagnetic characteristics of wireless communication channel in the unstructured environment, and verifies them by numerical simulation. This paper also studies the strategy of repeater's laying for the robot, thereby providing a reliably theoretical foundation to quickly start up the emergency wireless of the coal mine rescue robot.
In a fully mechanized mining face, there is interference between the hydraulic support face guard and the shearer drum. The two collisions seriously affect coal mine production and personnel safety. The identification of a shearer drum can be affected by fog generated when the shearer drum cuts forward. It is hydraulic support face guard recovery, not the timely block shearer drum, that will also affect the recognition of the shearer drum. Aiming at the above problems, a shearer drum identification method based on improved YOLOv5s with dark channel-guided filtering defogging is proposed. Aiming at the problem of fog interference affecting recognition, the defogging method for dark channel guided filtering is proposed. The optimal value of the scene transmittance function is calculated using guided filtering to achieve a reasonable defogging effect. The Coordinate Attention (CA) mechanism is adopted to improve the backbone network of the YOLOv5s algorithm. The shearer drum features extracted by the C3 module are reallocated by the attention mechanism to the weights of each space and channel. The information propagation of a shearer drum’s features is enhanced by such improvements. Thus, the detection of shearer drum targets in complex backgrounds is improved. S Intersection over Union (SIoU) is used as a loss function to improve the speed and accuracy of the shearer drum. To verify the effectiveness of the improved algorithm, multiple and improved target detection algorithms are compared. The algorithm is deployed at Huangling II mine. The experimental results present that the improved algorithm is superior to most target detection algorithms. In the absence of object obstruction, the improved algorithm achieved 89.3% recognition accuracy and a detection speed of 48.8 frame/s for the shearer drum in the Huangling II mine. The improved YOLOv5s algorithm provides a basis for identifying interference states between the hydraulic support face guard and shearer drum.
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