基于改进YOLO v4光线模糊场景下交通标志检测

近些年，自动驾驶开始进入人们的视线。对于自动驾驶而言，模糊光线场景下的交通标志检测是其中极其重要的一部分。目前YOLO v4算法广泛用于目标检测，虽然它的检测精度相比于其他YOLO版本有着较大的提高，但是还没有达到预期的精度。为了进一步提高检测交通标志的精度，本文在原有YOLO v4的基础上作一定的改进并与MSRCR图像增强处理相结合。首先将作为训练的图片通过MSRCR算法达到图像增强的目的，并将其作为目标检测的训练集图像。使用Darknet-53的YOLO v4网络，通过labelImg标注BelgiumTS交通信号数据集，使用改进的K-means++聚类算法确定先验框和具体参数并且改进路径聚合网络(PANet)结构和损失函数，将数据集进行训练。实验结果表明，改进后的算法与原本的YOLO v4算法相比较，平均精度提高了1.86个百分点。

Traffic Sign Detection in Blurred Light Scenes Based on Improved YOLO v4

In recent years, autonomous driving has begun to come into people’s sight. For autonomous driving, traffic sign detection in fuzzy light scene is an extremely important part. At present, YOLO v4 algorithm has been widely used in target detection, although its detection accuracy is greatly improved compared with other versions, but it has not reached the expected accuracy. In order to further improve the accuracy of detecting traffic signs, this article makes certain improvements on the basis of the original YOLO v4 and combines it with MSRCR image enhancement processing. Firstly, the original training images are enhanced by MSRCR algorithm, and it is used as the training set image of target detection. This article uses Darknet-53’s YOLO v4 network, labeles BelgiumTS traffic signal data set by labelImg, and uses the improved K-means++ clustering algorithm to determine the  priori box and specific parameters, and improves the path aggregation network（PANet） structure and loss function to train the data set. Experimental results show that compared with the original YOLO v4 algorithm, the improved algorithm has an average accuracy increase of 1.86 percentage points.