Traffic Sign Detection Method of Improved SSD Based on Deep Learning

Aiming at the problem of the traffic signs detects with poor precision, an improved Single Shot MultiBox Detector (SSD) detection algorithm is proposed. Firstly, the high-layer feature maps used for detection on the basis of the SSD algorithm are removed. Secondly, adjust the low-layer feature maps scale and aspect ratio to distribute more default boxes at lower layers, thereby enriching the fine features in the traffic sign scene map. Finally, collection of a large number of real traffic signs scene as an experimental data set. The experimental results show that the proposed algorithm has good robustness to traffic with different environmental conditions, and the mean Average Precision (mAP) is 0.7528, and has raised about 10% compared to the classical SSD, which verifies the effectiveness of the proposed algorithm.