An efficient approach for traffic sign detection, classification, and localization applied for autonomous intelligent vehicles

In this paper, an efficient approach for traffic sign detection, classification, and localization is introduced. An integration of the tiling technique with the RetinaFace model and the MobileNetV1-SSD was proposed for traffic detection and classification. The combination of the extraction of the region of interest with a depth estimation model, namely the AANet+, was introduced for the traffic sign localization task. All models were developed based on the transfer learning technique and existing datasets, including the Zalo and ApolloScape datasets. The accuracy and the computational efficiency of the approach are evaluated. Experimental results show that the novel traffic sign detection and classification method outperform the existing ones with an average precision of 77.2%. Moreover, the computing performance achieved is 5 FPS on Jetson Nano and 50 FPS on Jetson Xavier. For the traffic sign localization, the relative error can be reduced to 3.78%, and the computing time is 1.965 s/pair on Jetson Nano and 0.128 s/pair on Jetson Xavier, while the existing method has lower accuracy and is more time-consuming.