基于YOLOv8的雾天车辆行人实时检测方法

doi:10.16088/j.issn.1001-6600.2024120101

Abstract

Abstract: With the extensive application of intelligent communication technology in smart traffic scenarios, the task of detecting pedestrians and vehicles constitutes an important technical means for road safety. In light of the high missed detection rate and slow detection speed in the foggy environment, a real-time foggy target detection method based on YOLOv8 is proposed. The model incorporates the fog removal network module into the input image to preprocess it, retains the detailed features of the original image and eliminates the obstruction of fog, and then utilizes the improved YOLOv8n for detection. On YOLOv8n, the C2f module is enhanced based on FasterNet to reduce the model parameters and size, increase the model’s computing efficiency, and the SE-ResNeXt detection head is designed to avoid the negative impacts of stacking neural network layers. Finally, knowledge distillation is employed to further enhance the detection accuracy. The proposed model is validated on the RTTS dataset and the synthetic foggy dataset. Compared with the original network, the average precision (mAP@50_95) is improved by 5.2 percentage points, and the detection frame rate reaches 170 frame/s.

Key words: foggy scene, object detection, information interactive;Fasternet, SE-Net, ResNeXt

CLC Number: TP391.41

TANG Liang, CHEN Bowen, NIU Yisen, MA Ronggeng. A YOLOv8-based Real-time Object Detection Method for Vehicles and Pedestrians in Foggy Weather[J].Journal of Guangxi Normal University(Natural Science Edition), 2025, 43(3): 72-83.

References

[1] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2016: 779-788. DOI: 10.1109/CVPR.2016.91.
[2]LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[C]// Computer Vision-ECCV 2016. Cham: Springer, 2016: 21-37. DOI: 10.1007/978-3-319-46448-0_2.
[3]LI B, CHEN Y H, XU H, et al. Fast vehicle detection algorithm based on lightweight YOLO7-tiny[EB/OL]. (2023-04-17)[2024-12-01]. https://arxiv.org/abs/2304.06002. DOI: 10.48550/arXiv.2304.06002.
[4]GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]// 2014 IEEE Conference on Computer Vision and Pattern Recognition. Los Alamitos, CA: IEEE Computer Society, 2014: 580-587. DOI: 10.1109/CVPR.2014.81.
[5]REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. DOI: 10.1109/TPAMI.2016.2577031.
[6]WANG J W, CHEN C, WANG C W. Street sign recognition algorithm based on deep learning[C]// Proceedings of the 2020 3rd International Conference on Image and Graphics Processing. New York: Association for Computing Machinery, 2020: 31-35. DOI: 10.1145/3383812.3383819.
[7]李轩,李静,王海燕.复杂气象条件下的交通场景目标检测算法研究[J].计算机仿真,2021,38(2):87-90,105. DOI: 10.3969/j.issn.1006-9348.2021.02.020.
[8]LI G F, YANG Y F, QU X D. Deep learning approaches on pedestrian detection in hazy weather[J]. IEEE Transactions on Industrial Electronics, 2020, 67(10): 8889-8899. DOI: 10.1109/TIE.2019.2945295.
[9]LI B Y, PENG X L, WANG Z Y, et al. AOD-Net: all-in-one dehazing network[C]// 2017 IEEE International Conference on Computer Vision (ICCV). Los Alamitos, CA: IEEE Computer Society, 2017: 4780-4788. DOI: 10.1109/ICCV.2017.511.
[10]院老虎,翟柯嘉,张泽鹏,等.基于模拟雾天遥感数据集的飞机目标检测研究[J].南京邮电大学学报(自然科学版),2021,41(3):77-84. DOI: 10.14132/j.cnki.1673-5439.2021.03.011.
[11]MA Y H, CAI J F, TAO J X, et al. Foggy image detection based on DehazeNet with improved SSD[C]// Proceedings of the 2021 5th International Conference on Innovation in Artificial Intelligence. New York: Association for Computing Machinery, 2021: 82-86. DOI: 10.1145/3461353.3461363.
[12]HAN X F. Modified cascade RCNN based on contextual information for vehicle detection[J]. Sensing and Imaging, 2021, 22(1): 19. DOI: 10.1007/s11220-021-00342-6.
[13]董丽丽,丁畅,许文海.基于直方图均衡化图像增强的两种改进方法[J].电子学报,2018,46(10):2367-2375. DOI: 10.3969/j.issn.0372-2112.2018.10.009.
[14]RAIKWAR S C, TAPASWI S, CHAKRABORTY S. Bounding function for fast computation of transmission in single image dehazing[J]. Multimedia Tools and Applications, 2022, 81(4): 5349-5372. DOI: 10.1007/s11042-021-11752-9.
[15]JU M Y, DING C, REN W Q, et al. IDE: image dehazing and exposure using an enhanced atmospheric scattering model[J]. IEEE Transactions on Image Processing, 2021, 30: 2180-2192. DOI: 10.1109/TIP.2021.3050643.
[16]HE K M, SUN J, TANG X O. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2341-2353. DOI: 10.1109/TPAMI.2010.168.
[17]LI S Y, LIN J, YANG X, et al. BPFD-Net: enhanced dehazing model based on pix2pix framework for single image[J]. Machine Vision and Applications, 2021, 32(6): 124. DOI: 10.1007/s00138-021-01248-9.
[18]陈清江,张雪.混合残差学习与导向滤波算法在图像去雾中的应用[J].光学精密工程,2019,27(12):2702-2712. DOI: 10.3788/OPE.20192712.2702.
[19]CHEN J R, KAO S H, HE H, et al. Run, don’t walk: chasing higher FLOPS for faster neural networks[C]// 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2023: 12021-12031. DOI: 10.1109/CVPR52729.2023.01157.
[20]LIU G L, SHIH K J, WANG T C, et al. Partial convolution based padding[EB/OL]. (2018-11-28)[2024-12-01]. https://arxiv.org/abs/1811.11718. DOI: 10.48550/arXiv.1811.11718.
[21]孙锦,尹明锋,谢涛,等.基于多尺度特征融合的雾天目标检测方法[J].激光杂志,2024,45(6):106-113. DOI: 10.14016/j.cnki.jgzz.2024.06.106.
[22]IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[EB/OL]. (2015-03-02)[2024-12-01]. https://arxiv.org/abs/1502.03167. DOI: 10.48550/arXiv.1502.03167.
[23]CHEN Y H, TANG X, QI X B, et al. Learning graph normalization for graph neural networks[J]. Neurocomputing, 2022, 493: 613-625. DOI: 10.1016/j.neucom.2022.01.003.
[24]娄铮铮,张欣,胡世哲,等.一种基于YOLOX_s的雾天场景目标检测方法[J].计算机科学,2024,51(7):206-213. DOI: 10.11896/jsjkx.230400086.
[25]HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2016: 770-778. DOI: 10.1109/CVPR.2016.90.
[26]XIE S N, GIRSHICK R, DOLLÁR P, et al. Aggregated residual transformations for deep neural networks[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2017: 5987-5995. DOI: 10.1109/CVPR.2017.634.
[27]SZEGEDYC, IOFFE S, VANHOUCKE V, et al. Inception-v4, inception-ResNet and the impact of residual connections on learning[EB/OL]. (2016-08-23)[2024-12-01]. https://arxiv.org/abs/1602.07261. DOI: 10.48550/arXiv.1602.07261.
[28]HU J, SHEN L, ALBANIE S, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011-2023. DOI: 10.1109/TPAMI.2019.2913372.
[29]YANG Z D, LI Z, SHAO M Q, et al. Masked generative distillation[C]// Computer Vision-ECCV 2022. Cham: Springer, 2022: 53-69. DOI: 10.1007/978-3-031-20083-0_4.
[30]院老虎,常玉坤,刘家夫.基于改进YOLOv5s的雾天场景车辆检测方法[J].郑州大学学报(工学版),2023,44(3): 35-41. DOI: 10.13705/j.issn.1671-6833.2023.03.005.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

A YOLOv8-based Real-time Object Detection Method for Vehicles and Pedestrians in Foggy Weather

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 6

Metrics

Comments

Recommended 0

[1]	GUO Xiangyu, SHI Tianyi, CHEN Yannan, NAN Xinyuan, CAI Xin. Research on Foreign Object Detection in Railway Overhead Contact System Based on YOLO-CDBW Model [J]. Journal of Guangxi Normal University(Natural Science Edition), 2025, 43(2): 56-69.
[2]	LIU Yuna, MA Shuangbao. Fabric Defect Detection Based on Improved Lightweight YOLOv8n [J]. Journal of Guangxi Normal University(Natural Science Edition), 2025, 43(2): 83-94.
[3]	TU Zhirong, LING Haiying, LI Guo, LU Shenglian, QIAN Tingting, CHEN Ming. Lightweight Passion Fruit Detection Method Based on Improved YOLOv7-Tiny [J]. Journal of Guangxi Normal University(Natural Science Edition), 2024, 42(5): 79-90.
[4]	LÜ Hui, LÜ Weifeng. Fundus Hemorrhagic Spot Detection Algorithm Based on Improved YOLOv5 [J]. Journal of Guangxi Normal University(Natural Science Edition), 2024, 42(3): 99-107.
[5]	ZENG Liang, HU Qian, YANG Tengfei, TAN Weiwei. Substation Personnel Safety Operation Detection Based on L-ConvNeXt Network [J]. Journal of Guangxi Normal University(Natural Science Edition), 2024, 42(1): 102-110.
[6]	HUANG Yeqi, WANG Mingwei, YAN Rui, LEI Tao. Surface Quality Detection of Diamond Wire Based on Improved YOLOv5 [J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(4): 123-134.