The detection method for coal dust caused by chute discharge based on YOLOv4-tiny

Li Haibin; Sun Yuan; Zhang Wenming; Li Yaqian

doi:10.12086/oee.2021.210049

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Li H B, Sun Y, Zhang W M, et al. The detection method for coal dust caused by chute discharge based on YOLOv4-tiny[J]. Opto-Electron Eng, 2021, 48(6): 210049. doi: 10.12086/oee.2021.210049

Citation:

Li H B, Sun Y, Zhang W M, et al. The detection method for coal dust caused by chute discharge based on YOLOv4-tiny[J]. Opto-Electron Eng, 2021, 48(6): 210049. doi: 10.12086/oee.2021.210049

The detection method for coal dust caused by chute discharge based on YOLOv4-tiny

1.
School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
2.
Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China

Fund Project: Natural Science Foundation of Hebei Province (F2019203195)

More Information

^*Corresponding author: Sun Yuan, E-mail: 2786765531@qq.com

Received Date 09 February 2021

Revised Date 15 May 2021

Published Date 01 June 2021

Abstract

Abstract

The coal port will produce dust in the process of unloading coal by the chute of the ship loader. In order to solve the problem of dust detection, this paper proposes a method of coal dust detection based on deep learning (YOLOv4-tiny). The improved YOLOv4-tiny network is used to train and test the dust data set of chute discharge. Because the detection algorithm cannot get the dust concentration, this paper divides the dust into four categories for detection, and finally counts the area of detection frames of the four categories of dust. After that, the dust concentration is approximately judged through the weighted sum calculation of these data. The experimental results show that the detection accuracy (AP) of four types of dust is 93.98%, 93.57%, 80.03% and 57.43%, the average detection accuracy (mAP) is 81.27% (which is close to 83.38% of YOLOv4), and the detection speed (FPS) is 25.1 (which is higher than 13.4 of YOLOv4). The algorithm can balance the speed and accuracy of dust detection, and can be used for real-time dust detection to improve the efficiency of suppressing coal dust generated by chute discharge.
- coal dust detection /
- YOLOv4-tiny /
- deep learning /
- object detection

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References

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Overview

Overview

Overview: In recent years, with the improvement of the public's environmental protection concept and the tightening of environmental protection policies, how to effectively reduce or quickly suppress the dust generated in the production process has become a problem that coal ports must face. It is the last link of the whole production link that the ship loader unloads the coal to the cargo ship through the chute. In the front production link of the coal ports, various dust suppression measures are adopted, such as wind proof and dust suppression nets, dry dust removal systems, and so on. Although these measures effectively reduce the dust generation when the coal spills into the cabin, the dust suppression measures in the early stage do not form a closed-loop control with the unloading link, and cannot automatically suppress the dust from the unloading link. Thus, the separate treatment of the unloading dust is still an important part of the whole environmental protection operation. At present, the main measure to suppress this kind of dust in domestic coal ports is watering. As the discharge dust only occurs occasionally, if the sprinkler dust removal device is always turned on, it will lead to excessive watering, which will undoubtedly reduce the actual coal loading of cargo ships and affect the economic benefits. If the dust removal device is manually controlled by workers on site, it is not conducive to the construction of unmanned ports. Therefore, it is necessary to develop an automatic real-time detection method of coal dust discharge. When the dust is detected, an early warning signal is sent to inform the dust suppression operation to take corresponding measures. The improved deep convolution neural network (YOLOv4-tiny) is used to train and test on the data set of dust, and then to learn its internal feature representation. The improvement measures include: a SERes module is proposed to strengthen the information interaction between the detection algorithm network channels; a XRes module is proposed to increase the depth and width of the algorithm network; a SPP module and a PRN module are added to enhance the feature fusion ability of the algorithm. Because the detection algorithm cannot get the dust concentration, this paper divides the dust into four categories for detection, and finally counts the total area of the detection frame of the four categories of dust. After that, the dust concentration is approximately judged by weighting and calculating these data. The experimental results show that the detection accuracy (AP) of four types of dust is 93.98%, 93.57%, 80.03% and 57.43%, the average detection accuracy (mAP) is 81.27% (which is close to 83.38% of YOLOv4), and the detection speed (FPS) is 25.1 (which is higher than 13.4 of YOLOv4). The algorithm can balance the speed and accuracy of dust detection, and can be used for real-time dust detection to improve the efficiency of suppressing coal dust generated by chute discharge.