计算机科学
棱锥(几何)
能见度
人工智能
过程(计算)
算法
特征(语言学)
骨干网
计算机视觉
图像(数学)
模式识别(心理学)
数学
电信
语言学
哲学
物理
几何学
光学
操作系统
作者
Hanwei Mao,Peng Wang,Liming Zhou,Zhiren Zhu,Xiangmeng Ren
出处
期刊:Academic journal of science and technology
[Darcy & Roy Press Co. Ltd.]
日期:2024-09-13
卷期号:12 (2): 144-147
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI