Identification and intrusion early warning of large-scale engineering vehicles in opencast coal mines based on YOLOv8 algorithm

doi:10.16265/j.cnki.issn1003-3033.2024.S1.0032

Abstract

Abstract:

To ensure the safety of miners, protect equipment, and improve production efficiency, the identification and intrusion early warning technology of large-scale engineering vehicles in opencast coal mines based on multi-sensor information fusion was studied. Firstly, based on the overall technical framework and implementation method, the identification and detection method of engineering vehicles based on YOLOv8 was proposed. The software and hardware platform for detection was built in the opencast coal mine, and the identification accuracy of the engineering vehicle identification and detection method based on YOLOv8 was tested through a total of 6 300 sample datasets from on-site shooting and networks. The results show that the engineering vehicle detection model based on YOLOv8 can quickly and accurately identify multiple vehicle targets in the opencast coal mine, and the detection accuracy is more than 0.85, with a low missed detection rate. In addition, the incomplete vehicle image can be recognized. The engineering vehicle identification and intrusion early warning system studied in this paper provides vehicle identification and early warning hints for the working equipment to avoid safety accidents.

Key words: YOLOv8, opencast coal mine, large-scale engineering vehicle, target identification, intrusion early warning

CLC Number:

X924.3安全监控系统

SUN Guosi, MA Pengfei, WANG Weichun, GAO Puhao, ZHU Jianwei. Identification and intrusion early warning of large-scale engineering vehicles in opencast coal mines based on YOLOv8 algorithm[J]. China Safety Science Journal, 2024, 34(S1): 191-198.

Figures/Tables 10

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参数	数值	参数	数值
起始学习率	0.01	hsv_h	0.015
终点学习率	0.01	hsv_s	0.7
余弦学习率	Flase	hsv_v	0.4
优化器	ADAM	deterministic	TRUE
标签平滑参数	0	translate	0.1
正则化	0	scale	0.5
Momentum	0.94	epochs	500
Weight_decay	5×10^-4	imgsz	640
warmup_epochs	3	flipud	0
warmup_momentum	0.8	fliplr	0.5
warmup_bias_lr	0.1	half	TRUE
box	0.05	conf	0.5
cls	0.5	line_thickness	8
cls_pw	1	iou	0.7
obj	1	max_det	300
obj_pw	1	anchor_t	4
iou_t	0.2	split	val

类别	标签	训练集数量	验证集数量
挖掘机	Excavator	920	230
装载机	Loader	768	192
自卸卡车	Dumb_truck	792	198
移动式起重机	Mobile_crane	712	178
压路机	Roller	576	144
推土机	Bull_dozer	640	160
平地机	Grader	632	158

类别	标签	YOLOv8算法	AlexNet 算法
挖掘机	Excavator	0.949	0.893
装载机	Loader	0.884	0.886
自卸卡车	Dumb_truck	0.989	0.902
移动式起重机	Mobile_crane	0.990	0.862
压路机	Roller	0.994	0.912
推土机	Bull_dozer	0.886	0.825
平地机	Grader	0.921	0.868