Improved Target Detection Algorithm for Foggy Conditions in YOLOv8
DOI:
https://doi.org/10.54097/nnr7mg77Keywords:
Target Detection; YOLOv8; BiFPN; Shuffle Attention.Abstract
In a foggy environment, due to the significant decrease in visibility, the target image captured by the vehicle camera becomes blurred and the information is incomplete, which exacerbates the problem of misdetection and omission in the process of target detection, and poses a serious challenge to driving safety and navigation accuracy. In this regard, a foggy target detection algorithm based on improved YOLOv8 is proposed. First, by introducing a bidirectional feature pyramid BiFPN structure in the backbone network, the algorithm is able to better capture target detection features through bidirectional connections. Secondly, the Shuffle Attention mechanism is added to the neck network to enhance the diversity of the input sequences by randomly disrupting and grouping them, thus improving the performance of the self-attention mechanism, and thus improving the detection accuracy of the network model. The experimental results show that the average accuracy of the improved model on the RTTS dataset is increased by 1.3% mAP@0.5 and 1.2% by mAP@0.5:0.95. In summary, the improved model YOLO_BIS can show good performance when dealing with target detection tasks in foggy scenarios.
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[1] YANG Lei, CHEN Yanfei, LI Haiming, et al. Target detection algorithm for autopilot scene based on improved YOLOv8 [J/OL]. Computer Engineering and Application,1-17[2024-09-02].
[2] Su Tong, Wang Ying, Deng Qiyang, et al. The improved pedestrian and vehicle detection algorithm [J / OL]. Journal of System Simulation, 2024:1-11.
[3] Li Rensi, Shi Yunyu, Liu Xiang, et al. Image object detection based on Double-Head [J]. Liquid Crystal crystal and Display, 2023, 38 (12): 1717-1727.
[4] Qiang Zhang, Xiaojian Hu. MSFFA-YOLO Network: Multiclass Object Detection for Traffic Investigations in Foggy Weather.IEEE T. Instrumentation and Measurement, 2023, 72: 1-12.
[5] WEI Liu-Mei,LUO Xue-Mei,KANG Jian. Improved small target detection algorithm for aerial images with YOLOv8 [J/ OL]. Computer Engineering and Science,1-13[2024-09-02].
[6] M. Tan, R. Pang and Q. V. Le, EfficientDet: Scalable and Efficient Object Detection, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 10778-10787, doi: 10.1109/ CVPR 42600. 2020.01079.
[7] Liu, Lingzhi. Research on target detection algorithm based on multi-scale attention and dense connected network[D]. Guilin University of Electronic Science and Technology, 2023. DOI:10. 27049/d.cnki.ggldc.2023.001546.
[8] Q. -L. Zhang and Y. -B. Yang, SA-Net: Shuffle Attention for Deep Convolutional Neural Networks, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada, 2021, pp. 2235-2239, doi: 10.1109/ICASSP39728.2021.9414568.
[9] Yuxi Cheng. Research on traffic sign recognition in foggy weather combining GAN and YOLOv7[D]. Liaoning University of Engineering and Technology, 2023.DOI: 10. 27210/ d.cnki.glnju.2023.000096.
[10] Zhu Yan, Zhang Yuexia. SEP-YOLO: Improved road target detection algorithm based on YOLOv8[J/OL]. Computer Applications and Software,1-8[2024-09-02].
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