Foggy Road Environment Perception Algorithm Based on an Improved CycleGAN and YOLOv8

doi:10.12141/j.issn.1000-565X.240225

Abstract

Abstract:

In response to the issue of reduced road environment perception accuracy for intelligent vehicles under extreme haze conditions, this paper proposed a joint haze environment perception algorithm based on an improved CycleGAN and YOLOv8. Firstly, the CycleGAN algorithm was used as the framework for image defogging preprocessing. A self-attention mechanism was incorporated into the generator network to enhance the network’s feature extraction capability. Additionally, to minimize color discrepancies with real images, a self-regularized color loss function was introduced. Secondly, in the object detection phase, the lightweight GhostConv network was first used to replace the original backbone network, reducing computational complexity. Furthermore, the GAM attention mechanism was added to the neck network to effectively improve the network’s ability to interact with global information. Finally, the WIoU loss function was used to mitigate harmful gradients caused by low-quality samples, improving the model’s convergence speed. Experiments conducted on the RESIDE and BDD100k datasets validate the proposed algorithm. Results show that the structural similarity between dehazed and original images is 85%. Compared to the original CycleGAN algorithm and the AODNet algorithm, the proposed approach improves the peak signal-to-noise ratio (PSNR) by 2.24 dB and 2.5 dB, respectively, and the structural similarity index (SSIM) by 15.4% and 36.3%, respectively. Additionally, the improved YOLOv8 algorithm demonstrates enhancements over the original algorithm, with precision, recall, and mean average precision (mAP) increasing by 2.5%, 1.8%, and 1.1%, respectively. The experimental results confirm that the proposed algorithm outperforms traditional algorithms in terms of recall and detection accuracy, demonstrating its practical value

Key words: intelligent vehicle, environmental perception, image dehazing, CycleGAN, object detection, YOLOv8

CLC Number:

TP391.4

YUE Yongheng, LEI Wenpeng. Foggy Road Environment Perception Algorithm Based on an Improved CycleGAN and YOLOv8[J]. Journal of South China University of Technology(Natural Science Edition), 2025, 53(2): 48-57.

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算法	X_SNR/dB	S_SIM
自适应Retinex	10.849 0	0.391
AODNet	20.495 4	0.595
CycleGAN	19.854 0	0.704
改进CycleGAN	22.101 7	0.858

方法	X_SNR/dB	S_SIM
原CycleGAN模型	19.854 0	0.704
+自注意力机制	21.083 7	0.816
+自注意力机制+自正则化颜色损失函数	22.101 7	0.858

实验组别	模块			P/%	P_M/%	参数量/10⁶
实验组别	GhostConv	GAM	WIoU	P/%	P_M/%	参数量/10⁶
1	×^1）	×	×	58.9	46.6	3.01
2	√^2）	×	×	55.9	46.5	2.82
3	√	√	×	61.1	47.1	3.70
4	√	√	√	61.4	47.7	3.70

实验组别	模块			P/%	P_M/%	参数量/10⁶
实验组别	GhostConv	GAM	WIoU	P/%	P_M/%	参数量/10⁶
1	×	×	×	82.6	83.2	3.01
2	√	×	×	80.5	82.7	2.82
3	√	√	×	84.2	84.8	3.70
4	√	√	√	84.5	85.7	3.70

算法	P_A/%						P_M/%	R/%	v_FPS/（帧·s^-1）	参数量/10⁶
算法	Person	Car	Bus	Truck	Bike	Motor	P_M/%	R/%	v_FPS/（帧·s^-1）	参数量/10⁶
SSD	19.1	45.0	36.4	29.9	25.9	22.2	29.7	30.0	79.13	26.29
Faster R-CNN	41.0	70.8	52.5	46.1	25.7	24.3	43.4	41.6	43.31	137.10
YOLOv5	41.3	69.2	50.6	45.2	25.4	26.1	43.0	38.4	86.95	1.90
YOLOv8	48.5	73.6	53.2	49.8	29.8	25.1	46.6	42.9	90.91	3.20
改进YOLOv8	48.0	73.0	56.6	48.4	30.5	29.7	47.7	44.7	81.30	3.70