基于YOLOv8算法的露天煤矿大型工程车辆识别与闯入安全预警

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

摘要/Abstract

摘要：

为了保障矿工安全、保护设备及提高生产效率,提出基于多传感器信息融合的露天煤矿大型工程车辆识别与闯入预警技术。首先,在整体技术架构和实现方法设计的基础上,提出基于YOLOv8的工程车辆识别与检测方法;然后,在露天煤矿现场搭建检测软硬件平台,通过现场拍摄、网络搜集共计6 300张样本数据集,测试基于YOLOv8的工程车辆识别与检测方法的识别精度。结果表明:文中所提出的基于YOLOv8工程车辆检测模型,可以快速准确地识别露天煤矿作业现场内出现的多个车辆目标,检测准确率在0.85以上,漏检率较低,而且,能够识别不完整的车辆图像。提出的工程车辆识别与闯入预警系统,可为工作中的设备提供来车识别与预警提示,有助于预防安全事故的发生。

关键词: YOLOv8, 露天煤矿, 大型工程车辆, 目标识别, 闯入安全预警

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

中图分类号:

X924.3安全监控系统

孙国思, 马鹏飞, 王威淳, 郜普浩, 朱健伟. 基于YOLOv8算法的露天煤矿大型工程车辆识别与闯入安全预警[J]. 中国安全科学学报, 2024, 34(S1): 191-198.

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.

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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