Year: 2024
Type: article
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% [email protected] and 1.2% by [email protected]:0.95. In summary, the improved model YOLO_BIS can show good performance when dealing with target detection tasks in foggy scenarios.
DOI: 10.54097/nnr7mg77
Institution: Tianjin University of Technology and Education
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4402523470