基于YOLOv5的雾霾天气下交通标志识别

针对雾霾天气下道路交通标志识别难度大、精确度较低的问题，提出一种基于YOLOv5的雾霾天气交通标志识别模型。首先在YOLOv5原始模型上融入卷积注意力机制，在空间维度和通道维度上进行特征增强，抑制雾霾天气对模型的干扰；然后将BiFPN作为neck层中的特征融合结构，更加充分地融合多尺度特征，减少目标信息丢失；并选用CIoU作为YOLOv5的损失函数提高定位能力；使用K-means聚类算法在TT100K和CODA数据集重新获取锚框值，加快模型收敛速度。实验结果表明，改进后模型识别精度达到92.5%,比YOLOv5提升5.6%,在雾霾天气下仍能准确识别交通标志，速度达27 FPS,能够进行实时检测。

Traffic sign recognition under fog weather based on YOLOv5

Aiming at the problem of high difficulty and low accuracy in road traffic sign recognition under haze weather, a traffic sign recognition model based on YOLOv5 was proposed. Firstly, the convolutional attention mechanism was integrated into the original YOLOv5 model to enhance features in the spatial dimension and channel dimension to suppress the interference of haze weather on the model. Then, BiFPN is used as the feature fusion structure in neck layer to more fully fuse multi-scale features and reduce the loss of target information. CIoU is used as the loss function of YOLOv5 to improve the positioning ability. K-means clustering algorithm was used to re-obtain anchor frame values in TT100K and CODA datasets to accelerate the convergence speed of the model. The experimental results show that the recognition accuracy of the improved model reaches 92.5%, which is 5.6% higher than that of YOLOv5, and it can still accurately identify traffic signs in haze weather. and the speed can reach 27 FPS, which can be used for real-time detection.