In order to accurately identify the traffic sign information under different road conditions, an improved deep learning method based on Faster RCNN model is proposed. Firstly, a multi-channel parallel full convolution neural network is designed to extract the color, shape and texture features of traffic signs in the original image. The multi-channel feature layers are fused to get the final feature map, and the adaptability of the model in various environment and weather conditions is enhanced by the image preprocessing. At the same time, the fusion features of deep and shallow feature layer are added into the feature extraction network, and the detailed texture information of shallow feature layer and semantic information of deep feature layer are retained, and the final feature layer can adapt to multi-scale change of traffic sign recognition. Secondly, the prior knowledge of traffic signs is used to detect and locate the target before the original RPN candidate region is generated. A more reasonable method for generating feature points and candidate anchor frames for traffic sign recognition is proposed. Based on the prior knowledge statistics of traffic sign size and proportion results, a target candidate frame suitable for traffic sign recognition is designed, a large number of redundant and negative correlation candidate frames is reduced, the detection accuracy and reduces the detection time is improved; secondly, the multi-scale candidate frame generation method for the deep and shallow feature layer is added to enhance the multi-scale target recognition ability and further strengthen the multi-scale target recognition ability Finally, this paper uses the international general traffic sign specification data set GTSRB/GTSDB and domestic traffic sign data set tt100k to verify the recognition ability of the model.
Traffic sign recognition with multi-scale Convolutional Networks
