Traffic sign detection and recognition based on residual single shot multibox detector model

The existing target detection methods were only suitable for large size and few specific types of traffic signs, and showed poor performance on complex traffic scene images. The ResNet101 with strong anti-degradation performance was used as basic network, and then a residual single shot multibox detector (SSD) model added with a number of convolution layers was proposed, in order to conduct multi-scale block detection on high resolution traffic images. A strategy Coarse-to-Fine was adopted to omit the prediction of pure background image blocks, in order to speed up. The target range was narrowed by the initial detection results of the medium scale image block. The other blocks within the target range were detected. All the block results were mapped back to the original image and non-maximum suppression was used to realize accurate recognition. Experiment results showed that the proposed method achieved 94% overall accuracy and 95% overall recall on the public traffic sign dataset Tsinghua-Tencent 100K. The detection ability on traffic sign with different sizes and shapes in multi-resolution images was strong and the proposed model was robust. © 2019, Zhejiang University Press. All right reserved.