Improved Traffic Sign Detection Algorithm for YOLOv5

Traffic sign detection is an important link in the fields of automatic driving and assisted driving, which is related to driving safety. Aiming at the difficulties of small targets and complex backgrounds in traffic signs, an algorithm based on improved YOLOv5 is proposed. Firstly, a regional context module is proposed, which uses dilated convolutions with various dilation rates to obtain different receptive fields, and then obtains the feature information of the target and its adjacent areas. The information of adjacent areas plays an important role in small objects detection in traffic signs. It can effectively solve the problem of small targets. Secondly, a feature enhancement module is introduced in the backbone part to further improve the feature extraction ability of the backbone, and the attention mechanism is combined with the original C3 module to make the network more focused on small target information and avoid complex backgrounds. Finally, in the multiscale detection part, the feature fusion of the shallow feature layer and the deep detection layer can take into account both the shallow position information and the deep semantic information, increase the target positioning accuracy and boundary regression, and is more conducive to small target detection. The experimental results show that the improved algorithm achieves 87.2% small target detection precision, 92.4% small target recall and 91.8% mAP on the traffic sign detection data set TT100K, which is improved by 3.5, 4.1 and 2.6 percentage points respectively compared with the original YOLOv5 algorithm, detection speed 83.3 frame/s. On the CCTSDB dataset, mAP is 98.0%, increases 2.0 percentage points, and the detection speed is 90.9 frame/s. Therefore, the proposed improved YOLOv5 algorithm can effectively improve the traffic signs detection precision and recall, and the detection speed is comparable. © 2016 Chinese Medical Journals Publishing House Co.Ltd. All rights reserved.