【摘要】 针对弱光线环境下道路交通标志检测精度不高、漏检、错检等情况，提出了一种改进YOLOX的融合检测算法。该算法引入轻量级Mobile Vi T Block模块，将CNN和Transformer结合，提高了网络对物体局部和全局特征的学习能力；通过添加自适应特征融合金字塔ASFF,对有效特征层进行加权融合，加快了网络训练收敛速度；并采用Focal Loss替换二元交叉熵损失函数，用以解决因样本少导致分类不准确的问题。实验结果表明，相较于YOLOX算法，改进YOLOX算法mAP值提升了2.89%,参数量减少了6.23 M,可视化实验进一步验证了所提算法可以提高检测精度，有效避免因弱光线导致的漏检、错检现象。更多还原

【Abstract】 In view of the low detection accuracy, missed detection, and the wrong detection of road traffic signs in a weak light environment, a detection algorithm based on the improved YOLOX is put forward. Light weight network named Mobile Vi T Block module is adopted, meanwhile CNN is combined with Transformer to raise the network’s ability to learn local and global features of objects. By adding the adaptive feature fusion pyramid ASFF, the improved algorithm performs weighted fusion on the effective feature layers in order to accelerate the convergence speed of network training. The binary cross-entropy loss function is replaced by a Focal Loss, so as to solve the problem of inaccurate classification due to the small samples size. As shown by the experimental results, the mAP value of the improved YOLOX algorithm is increased by 2.89% than that of the YOLOX algorithm, and the number of parameters is reduced by 6.23 M. The visuali-zation and other experiments further verify that the improved YOLOX algorithm can effectively avoid the phenomena of missing and wrong detection caused by weak light.更多还原