A fast target detection algorithm in severe road environment based on improved YOLOV₄-tiny

In order to improve the algorithm's detection ability in severe road scenes, a fast target detection algorithm based on YOLOV4-Tiny was proposed. First, in consideration of severe weather conditions, this paper combined the algorithm of ReBlur and the dark channel prior algorithm to process the images. On the foundation of the above results, the images before and after processing are used for network training and testing to overcome the problem of image quality degradation. On the other hand, for the detection of small targets, a detection head for small targets was added. The eight times down-sampling feature map was up-sampled for splicing with the feature map of the upper layer. The experimental results show that the improved network's detection ability is significantly improved in complex road scenes, and the overall mean average precision (mAP) value is also increased by 4.13%. Meanwhile, the detection speed reaches 213 FPS.