Automatic Recognition of Traffic Signs Based on Visual Inspection

Abstract

The automatic recognition of traffic signs is essential to autonomous driving, assisted driving, and driving safety. Currently, convolutional neural network (CNN) is the most popular deep learning algorithm in traffic sign recognition. However, the CNN cannot capture the poses, perspectives, and directions of the image, nor accurately recognize traffic signs from different perspectives. To solve the problem, the authors presented an automatic recognition algorithm for traffic signs based on visual inspection. For the accuracy of visual inspection, a region of interest (ROI) extraction method was designed through content analysis and key information recognition. Besides, a Histogram of Oriented Gradients (HOG) method was developed for image detection to prevent projection distortion. Furthermore, a traffic sign recognition learning architecture was created based on CapsNet, which relies on neurons to represent target parameters like dynamic routing, path pose and direction, and effectively capture the traffic sign information from different angles or directions. Finally, our model was compared with several baseline methods through experiments on LISA (Laboratory for Intelligent and Safe Automobiles) traffic sign dataset. The model performance was measured by mean average precision (MAP), time, memory, floating point operations per second (FLOPS), and parameter number. The results show that our model consumed shorter time yet better recognition performance than baseline methods, including CNN, support vector machine (SVM), and regionbased fully convolutional network (R-FCN) ResNet 101.

Traffic Sign Detection via Improved Sparse R-CNN for Autonomous Vehicles

“…Yang et al [22] used adversarial machine learning to generate adversarial examples in order to improve the detection robustness of autonomous vehicles but did not consider the effect of the environment on the detection. He et al [23] presented a traffic sign detection using CapsNet [24] based on visual inspection. However, it extracts HOG feature from images, which does not contain semantic information.…”

DetectFormer: Category-Assisted Transformer for Traffic Scene Object Detection

“…Single-stage detection methods directly detect the object and regress the bounding boxes different from multi-stage methods, which can avoid the repeated calculation of the feature map and obtains the anchor boxes directly on the feature map. He et al [19] proposed a detection method using CapsNet [20] based on visual inspection of traffic scenes. Li et al [21] proposed improved Faster R-CNN for multi-object detection in a complex traffic environments.…”

A Small Network MicronNet-BF of Traffic Sign Classification

“…Most of images are clear, but part of them is blurred and darkness used to test the algorithm's robustness. It not only allows researchers to test the accuracy of their algorithm and to compare it with human performance but also to be transformed by the histogram of the oriented gradient algorithm to prevent projection distortion [ 5 ] or denoised to promise the quality of dataset [ 6 ].…”

“…As a deep learning network, it has many layers to simulate neurons to learn the characters of images. It has showed high performance in many datasets such as CIFAR [ 11 ] and ImageNet [ 12 ], so people consider applying the enhanced CNN (e.g., LeNet-5 [ 13 ], Caps Net [ 5 ], PFANet [ 14 ], differential evolution evolved RBFNN [ 15 ], etc.) in traffic classification.…”