A Small Traffic Sign Detection Algorithm Based on Modified SSD

Traffic sign detection is an important part of many systems such as autonomous driving, driver safety and assistance. In this paper, the detection capability of SSD for small targets is analyzed and improved based on ssd_300 model. CSUST Chinese Traffic Sign Detection Benchmark (CCTSDB) dataset is used to train the model for Chinese road traffic conditions. The improved model was compared with ssd_300 model. The experimental results show that the mAP of the improved model on the test dataset achieves 0.85, which is 0.13 higher than ssd_300, and the algorithm can reach real-time detection. The improved model can effectively detect three categories of Chinese traffic signs and has strong robustness against various disturbances.

[1]  Jianming Zhang,et al.  A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2 , 2017, Algorithms.

[2]  Johannes Stallkamp,et al.  Detection of traffic signs in real-world images: The German traffic sign detection benchmark , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[3]  Yi Li,et al.  R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.

[4]  Xiaolin Hu,et al.  Traffic sign detection by ROI extraction and histogram features-based recognition , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[5]  Nojun Kwak,et al.  Enhancement of SSD by concatenating feature maps for object detection , 2017, BMVC.

[6]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[7]  Wenyu Liu,et al.  Traffic sign detection and recognition using fully convolutional network guided proposals , 2016, Neurocomputing.

[8]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Luis Moreno,et al.  Road traffic sign detection and classification , 1997, IEEE Trans. Ind. Electron..