Improved Target Detection Algorithm for Foggy Conditions in YOLOv8

Authors

  • Hanwei Mao
  • Peng Wang
  • Liming Zhou
  • Zhiren Zhu
  • Xiangmeng Ren

DOI:

https://doi.org/10.54097/nnr7mg77

Keywords:

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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References

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Published

14-09-2024

Issue

Section

Articles

How to Cite

Mao, H., Wang, P., Zhou, L., Zhu, Z., & Ren, X. (2024). Improved Target Detection Algorithm for Foggy Conditions in YOLOv8. Academic Journal of Science and Technology, 12(2), 144-147. https://doi.org/10.54097/nnr7mg77