基于注意力机制的雾天目标检测算法

Wanye Gu, Yuecheng Yu, Liming Cai, Jinlong Shi, Yongzheng Li, Shixin Huang
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引用次数: 0

摘要

本文提出了一种基于注意力机制的雾天目标检测模型,解决了常规目标检测模型直接应用于雾天场景时存在的检测精度低、检测漏检和误检等问题。首先,为了增强检测网络的多尺度表达能力和对目标的敏感性,用集成了注意机制的残差模块取代了骨干网的瓶颈模块;这种设计提高了网络在细粒度水平上提取特征和定位目标的能力。其次,用CIOU损失函数代替原有的损失函数,提高了边界盒回归过程的稳定性。第三,本文采用k -means++聚类算法生成适合数据集的锚点。此外,在大气散射模型的基础上,进一步丰富雾天场景下的目标检测数据集。实验结果表明,该方法在轻雾、中雾和浓雾场景下的mAP分别比原来的yolov5提高了7.4%、6.05%和6.36%。这种精度的提高显著降低了漏检率和误检率,有效提高了雾天条件下的目标检测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Object detection algorithm based on attention mechanism in foggy weather
In this paper, we propose a foggy weather object detection model based on an attention mechanism, to address the problem of low detection accuracy, missed detection and false detection when general object detection models are applied directly to foggy scenes. Firstly, to enhance the detection network's multi-scale expression ability and sensitivity to the target, a residual module that integrates the attention mechanism replaces the BottleNeck module of the backbone network. This design improves the network's ability to extract features and locate targets at a fine-grained level. Secondly, the CIOU loss function replaces the original loss function, improving the stability of the bounding box regression process. Thirdly, the K-means++ clustering algorithm is used to generate anchors suitable for the dataset in this paper. Furthermore, the object detection dataset in foggy scenes is further enriched based on the atmospheric scattering model. Experimental results indicate that the proposed method's mAP in light fog, medium fog and dense fog scenes is increased by 7.4%, 6.05% and 6.36%, respectively, compared to the original YOLOv5s. This improvement in accuracy significantly reduces the missed detection rate and false detection rate, effectively enhancing object detection performance in foggy weather.
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