Coal Mine Belt Conveyor Foreign Objects Recognition Method of Improved YOLOv5 Algorithm with Defogging and Deblurring

Abstract

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.

A Faster and Lighter Detection Method for Foreign Objects in Coal Mine Belt Conveyors

“…Taking safety factors into consideration, the validation was conducted at the National Joint Local Engineering Laboratory in Taiyuan, Shanxi Province, China. Additionally, this study further evaluated the generalization performance of the new model by using the dataset from [ 18 ]. The datasets are labeled as DataI and DataII, and the evaluation results are presented in Table 12 and Table 13 .…”

“…The differences between the methods of this study and those of other researchers are shown in Table 14 . Specifically, in terms of the richness of foreign object types, as compared to [ 14 , 15 , 18 , 19 ], the approach presented in this study exhibits superior performance in terms of both detection speed and accuracy by providing a more detailed classification of foreign objects transported (including six common types). In terms of the comparison of network model parameters and computational complexity, as compared to [ 16 , 17 ], the improved model in this study, although not achieving the highest accuracy, exhibits outstanding parameter efficiency (4.1 M) and FPS (92.5), which are more favorable for edge devices with limited computational capabilities.…”

“…Qinghua Mao et al [ 18 ] integrated adaptive spatial features and the Convolutional Block Attention Module (CBAM) attention mechanism into the YOLOv5 network model. This integration allows for the complete extraction of multi-level feature information from foreign object images, accurately identifying foreign objects.…”

A Machine Vision-Based Edge Detection Method for Belt Lap of Pipe Belt Conveyor

“…Conveyor belt torsion is commonly characterized by the rotation of the belt's lap position, exceeding an angle of 20° in either the clockwise or counterclockwise direction in relation to the structural frame of the conveyor [4]. At present, the detection of conveyor belt torsion may be categorized into two distinct approaches: the conventional method and the utilization of video-based artificial intelligence (AI) recognition technology [5][6].…”

Intelligent Identification Method of Shearer Drums Based on Improved YOLOv5s with Dark Channel-Guided Filtering Defogging

“…Mao [8] et al proposed a method based on boundary constraints and nonlinear background regularization, which is of great importance for the monitoring of fully mechanical mining faces. Mao [9] et al used a dark channel prior defogging algorithm to reduce the impact of fog on the clarity of surveillance videos. They used a custom convolution method to sharpen the image.…”