Zhan Huawei
College of Electronic and Electrical Engineering, Henan Key Laboratory of Optoelectronic Sensing Integrated Application， Henan Normal University,Xinxiang 453007,ChinaZou Haohao
College of Electronic and Electrical Engineering, Henan Key Laboratory of Optoelectronic Sensing Integrated Application， Henan Normal University,Xinxiang 453007,ChinaLiu Xu
College of Electronic and Electrical Engineering, Henan Key Laboratory of Optoelectronic Sensing Integrated Application， Henan Normal University,Xinxiang 453007,ChinaShi Shuie
College of Electronic and Electrical Engineering, Henan Key Laboratory of Optoelectronic Sensing Integrated Application， Henan Normal University,Xinxiang 453007,ChinaCollege of Electronic and Electrical Engineering, Henan Key Laboratory of Optoelectronic Sensing Integrated Application， Henan Normal University,Xinxiang 453007,China
TP391.41
This paper proposes a traffic sign detection method based on an SSD network. This method improves the existing SSD algorithm, which has low detection accuracy and weak generalization ability for small targets, and has problems such as false detection and missed detection. The ResNet-50 network is used as the backbone network of the SSD algorithm, and the BN layer is added to the additional layer to improve the training speed. Sub-pixel is used instead of upsampling to improve the resolution of the recognition target, and the MFPN model is added to fuse the low-level and high-level feature information to avoid the problem of missed detection. The experimental results show that the improved SSD algorithm improves the mAP value by 4.2% and 3.1% on the public datasets CCTSDB and GTSDB datasets, respectively, the FPS remains at 87.2f/s, and the detection accuracy is significantly improved. This work meets the requirements for real-time detection of traffic signs and has broad application prospects in the field of unmanned driving.
