Abstract
Detection and recognition of traffic signs are the keys to creating advanced driving assistance systems. Making highly precise maps also requires the identification and extraction of such road elements as traffic signs. Traditional detection and recognition methods no longer meet today’s needs, and object recognition algorithms based on deep learning have become the mainstream solution. However, current algorithms have limitations. The recognition speed of one-stage strategy algorithms is fast, but recognition accuracy is not satisfactory especially for small objects. The accuracy of two-stage algorithms is higher, but the recognition speed is extremely slow. This paper solves these problems with a proposed parallel attention convolution module, a channel attention pyramid network, and a loss function diagonal and center point IoU based on the YOLOv3 algorithm. The improved models in this paper are compared with SSD, YOLOv3, and Faster RCNN. Experimental results show that the proposed models have some improvement over the above models: the mAP of the models with PACM, CAFPN, and DCPIoU was 76.02%, compared with SSD300, SSD500, Faster RCNN, and YOLOv3, which had improvements of 9.27%, 6.93%, 2.94, and 5.3%, respectively. And the FPS of the improved model is basically the same as the original YOLOv3, without reducing the real-time performance.
Highlights
Autonomous driving has become a popular research topic recently
The parallel attention convolution module (PACM) and channel attention feature pyramid network (CAFPN) structures proposed in this paper enhance the attention of the model to small objects in terms of feature extraction and feature fusion, respectively, while the diagonal and center point IoU (DCPIoU) is proposed to improve model convergence
To solve the difficult problem of recognizing small traffic signs, this paper proposes PACM and a CAFPN for feature extraction and fusion of extracted features, respectively
Summary
Autonomous driving has become a popular research topic recently. The advanced driving assistance system is emerging as the first transition technology for autonomous driving at the L4 level or above. The accuracy and robustness of automatic recognition of traffic signs using deep learning significantly improve the traditional approach, which uses template matching and support vector machines. The result accurately locates traffic signs and performs real-time classification using a single fast Fourier transform. Another method first converts the image from color space to grayscale space [3], uses shape templates to obtain the region of interest, represents the features with HOG, and uses SVM for classification. Traditional methods can recognize traffic signs, the process is cumbersome, poor in extracting features, and inadequate in meeting the requirements of real-time autonomous driving. A deep learning method based on convolutional neural networks has emerged
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