A Lightweight and Multisource Information Fusion Method for Real‐Time Monitoring of Lump Coal on Mining Conveyor Belts

Abstract

Since the underground transportation of coal mainly relies on the mine conveyor belt to complete, the mine conveyor belt with large pieces of coal will affect transportation safety. Therefore, to address the problem of real-time monitoring of lump coal, the method Ghost-ECA-Bi FPN (GEB) YOLOv5 for lump coal in the process of mining conveyor belt transportation is proposed based on a lightweight neural network and multisource information fusion. First, the image preprocessing is performed by adaptive histogram equalization, which reduces the influence of coal dust, dust, and uneven lighting on target monitoring. Second, the redundancy of the convolution process is exploited, and a lightweight neural network GhostNet is introduced to optimize the feature extraction process. In addition, combined with the efficient channel attention mechanism, the 1D convolution enables local cross-channel information interaction, which can solve the problem of imbalance between model complexity and performance. Finally, the feature information of the three stages is fused using a weighted bidirectional feature pyramid network to enhance the generalization ability of the model. The experimental results show that the improved GEB YOLOv5 algorithm has obvious advantages. In terms of model structure, the number of network layers reduces by 36.97%, and the number of model structure parameters and floating-point operations reduce by 64.53% and 69.14%, respectively. Moreover, the model volume reduces from 92.7 M to 33.0 M. Regarding the monitoring performance, the precision and recall rates improve by 1.19% and 1.11%, respectively. Furthermore, the real-time performance improves from 68.34 FPS to 110.70 FPS. It can be seen that the problem of the model performance against the model complexity is effectively solved in this experiment and the real-time monitoring of lump coal is realized.

A Lump Coal Detection Method Fusion of Lightweight and Attention Mechanism

“…In order to verify the monitoring performance of the model in the lump coal dataset, the CBAM-YOLOv5 model from literature [6], the GES YOLOv5 model from literature [25] and the Ghost-C3SE YOLOv5 model from literature [26] were used to carry out comparative experiments of lump coal monitoring under the same equipment and parameters, respectively. The experimental results are shown in Table III.…”

SNW YOLOv8: improving the YOLOv8 network for real-time monitoring of lump coal

“…In this study, the lightweight performance of the SNW YOLOv8 conveyor belt lump coal real-time detection model is compared with the GEB YOLOv5 [18], LM YOLOv5 [20], and GSB YOLOv5 [19] algorithm models. Figure 6 depicts the results used to verify the lightweight performance of the model proposed in this work.…”

“…Our previous work [18] proposed a real-time detection method for lump coal using the YOLOv5 algorithm. It combined a lightweight neural network and a multifeature fusion mechanism to achieve a detection precision of 60.72%.…”

Research on Belt Deviation Fault Detection Technology of Belt Conveyors Based on Machine Vision

“…As an important part of coal mine production, the safety and stability of the operation of underground coal mine belt conveyors directly affect the safety and efficiency of coal production [7][8][9]. Belt conveyors can enable the long-distance, large capacity, continuous and stable transport of raw coal to the outside.…”