Mining truck driver fatigue driving detection based on improved YOLOv8

doi:10.16265/j.cnki.issn1003-3033.2025.01.0147

Abstract

Abstract:

To address the high rates of missed detections and false alarms, as well as the poor robustness in fatigue driving detection for open-pit mine truck drivers, a fatigue driving detection model for mine truck drivers (EBS-YOLO) based on the improved YOLOv8 is constructed to enhance the overall performance of fatigue detection. Firstly, YOLOv8 was used as the basic model for fatigue detection, and a small target detection layer was added to enhance the model's attention to small targets. Secondly, the bottleneck attention module (BAM) was used to improve the model performance to extract small target features, especially eye features. Finally, all cross-stage aggregation modules (C2f) in the backbone network were replaced with efficient multi-scale attention (EMA) modules, thereby effectively reducing model parameters and computational overhead to meet the requirements of a lightweight model. The results showed that the improved YOLOv8 model had a great detection effect with the accuracy, recall rate, and average detection accuracy reaching 93.6%, 93.9%, and 96.5%, respectively, and the memory size of the model was only 4.9 MB. Compared with the YOLOv8 model, the improved model can quickly and accurately identify the fatigue state of mining truck drivers, meet real-time requirements, and effectively prevent fatigue-driving accidents.

Key words: open-pit mines, fatigue driving detection, truck driver, detection of small targets, YOLOv8

CLC Number:

X910安全人体学

GU Qinghua, YIN Shutan, WANG Dan, LI Xuexian, YIN Huimin. Mining truck driver fatigue driving detection based on improved YOLOv8[J]. China Safety Science Journal, 2025, 35(1): 60-66.

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模型	mAP @0.5/%	Params/ M	FLOPs/ G	模型大小/ MB
YOLOv8n	91.2	3.01	8.1	6.3
小目标	94.1	10.95	37.8	22.1
小目标+Biformer	94.8	10.98	38.4	22.4
小目标+GAM	94.3	11.28	38.1	22.5
小目标+SimAM	94.6	10.94	37.8	22.3
小目标+CBAM	94.5	11.62	39.2	23.1
小目标+BAM	96.3	11.53	38.9	22.3

序号	小目标	BAM	EMA	mAP@0.5/%	mAP@0.5-0.95/%	Params/M	FLOPs/G	模型大小/MB
1	—	—	—	91.2	53.2	3.01	8.1	6.3
2	√	—	—	94.9	54.9	10.95	37.8	22.1
3	√	√	—	96.3	56.1	11.53	38.9	22.3
4	√	√	√	96.5	56.7	2.28	10.7	4.9

模型	mAP @0.5/%	Params/ M	FLOPs/ G	FPS/ (帧·s^-1)	模型大小/MB
YOLOv8n	91.2	3.01	8.1	51	6.3
YOLOv3-tiny	85.3	105.7	292.2	5	206.8
YOLOv5s	91.4	4.9	14.9	44	13.2
YOLOv6n	89.5	4.5	12.8	43	8.9
YOLOv7-tiny	90.5	6.7	15.1	41	72.6
SSD	93.8	26.4	107.8	25	93.5
Fast-RCNN	92.3	42.6	134.5	10	106.5
EBS-YOLO	96.5	2.28	10.7	49	4.9

模型	人脸	睁眼	闭眼	张嘴	闭嘴	打哈欠	戴口罩	低头	P/%	R/%	mAP@0.5/%
YOLOv8n	97.9	85.6	85.8	89.3	87.7	92.2	94.6	96.5	88.4	87.8	91.2
EBS-YOLO	98.5	95.4	96.2	96.6	95.9	96.5	97.1	95.8	93.6	93.9	96.5

模型	P/ %	R/ %	mAP @0.5/%	FPS/ (帧·s^-1)	模型大小/MB
YOLOv8n	91.4	91.0	92.4	52	6.3
YOLOv5s	92.1	85.0	91.9	46	13.2
YOLOv6n	84.1	88.2	89.2	44	8.9
YOLOv7-tiny	90.7	90.4	94.2	40	72.2
SSD	94.0	83.6	94.1	28	93.5
Fast-RCNN	92.9	82.1	93.3	19	106.5
EBS-YOLO	96.8	96.5	97.4	49	4.9