Detection of traffic signs based on lightweight YOLOv8s

doi:10.11996/JG.j.2095-302X.2024030422

Abstract

Abstract:

To enhance the real-time capability and feasibility of traffic sign detection, a lightweight traffic sign detection model based on YOLOv8s was proposed. Firstly, the BottleNeck in the C2f module was replaced with the residual module FasterNetBlock in FasterNet, reducing the model’s parameter count and computational complexity. Secondly, the large object detection layer was replaced with a small object detection layer, decreasing the number of network layers in Backbone and achieving a significant improvement in detection speed and a reduction in parameter count. Finally, the original complete intersection over union (CIOU) loss function was replaced with the wise intersection over union (Wise-IOU), thereby enhancing both speed and accuracy. Verified on the TT100K traffic sign dataset, compared with the YOLOv8s model, mAP50 increased by 5.16%, parameter count decreased by 76.48%, computational complexity decreased by 13.33%, and frames per second (FPS) improved by 35.83%. In comparison to other models, mAP50 exhibited an average increase of 15.11%, an average decrease of 85.74% in parameter count, an average decrease of 46.23% in computational complexity, and an average increase of 31.49% in FPS. This model achieved the advantages of high detection accuracy, small number of parameters, low computational complexity, and fast speed. It represented a substantial improvement over the original algorithm and demonstrated strong competitiveness when compared to other advanced traffic sign detection models, with great advantages in traffic sign detection.

Key words: lightweight, YOLOv8s, improved small target layer, traffic sign detection, Wise-IOU, TT100K

CLC Number:

ZHU Qiangjun, HU Bin, WANG Huilan, WANG Yang. Detection of traffic signs based on lightweight YOLOv8s[J]. Journal of Graphics, 2024, 45(3): 422-432.

Figures/Tables 17

References 34

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样本真实情况	预测结果
样本真实情况	正样本	负样本
正样本	TP	FN
负样本	FP	TN

样本真实情况	预测结果
样本真实情况	正样本	负样本
正样本	TP	FN
负样本	FP	TN

模型	P/%	R/%	mAP50/%	参数量/M	GFLOPs	FPS
YOLOv8s	85.34	78.39	87.18	11.14	28.5	32.15
YOLOv8s+小目标检测层	88.02	85.46	92.18	10.86	37.9	26.67
YOLOv8s+改进小目标检测层	87.39	84.71	91.71	3.53	31.0	37.88

模型	P/%	R/%	mAP50/%	参数量/M	GFLOPs	FPS
YOLOv8s	85.34	78.39	87.18	11.14	28.5	32.15
YOLOv8s+小目标检测层	88.02	85.46	92.18	10.86	37.9	26.67
YOLOv8s+改进小目标检测层	87.39	84.71	91.71	3.53	31.0	37.88

模型	小目标		中等目标		大目标
模型	AP/%	AR/%	AP/%	AR/%	AP/%	AR/%
YOLOv8s	47.20	62.24	73.36	82.27	85.23	88.55
YOLOv8s+小目标检测层	58.62	71.27	75.88	82.74	82.89	89.41
YOLOv8s+改进小目标检测层	55.19	77.18	75.30	68.71	84.77	89.02