Real-Time Detection Method for Small Traffic Signs Based on Yolov3
Huibing Zhang, Longfei Qin, Jun Li, Yunchuan Guo, Ya Zhou, Jingwei Zhang, Zhi Xu
Abstract
It is very challenging to detect traffic signs using a high-precision real-time approach in realistic scenes with respect to driver-assistance systems for driving vehicles and autonomous driving. To address this challenge, in this paper, a new detection scheme (named MSA_YOLOv3) is proposed to accurately achieve real-time localization and classification of small traffic signs. First, data augmentation is achieved using image mixup technology. Second, a multi-scale spatial pyramid pooling block is introduced into the Darknet53 network to enable the network to learn object features more comprehensively. Finally, a bottom-up augmented path is designed to enhance the feature pyramid in YOLOv3, and the result is to achieve accurate localization of objects by utilizing fine-grained features effectively in the lower layers. According to the tests on the TT100K dataset (which is a dataset for traffic sign detection), the performance of the proposed MSA_YOLOv3 is better than that of YOLOv3 in detecting small traffic signs. The detection speed of MSA_YOLOv3 is 23.81 FPS, and the mAP (mean Average Precision) reaches up to 86%.