Two‐stage traffic sign detection and recognition based on SVM and convolutional neural networks

Hechri, Ahmed; Mtibaa, Abdellatif

doi:10.1049/iet-ipr.2019.0634

Cited by 38 publications

(23 citation statements)

References 35 publications

(38 reference statements)

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“…According to the value of the cost function and recognition accuracy, the TDCNN network model has better generalization performance and higher recognition accuracy. Hechri and Mtibaa (2020) applied CNN to recognize the sub-categories of traffic signs and Support Vector Machine (SVM) to detect traffic signs; their method had low complexity, applicable to real-time processing [21]. This method has a similar effect to the TDCNN model proposed; nevertheless, the TDCNN model is more accurate for image classification and has better generalization performance.…”

Section: Figure 4 Training Results Of Tdcnn Network Model Under Diffmentioning

confidence: 99%

Image Recognition and Safety Risk Assessment of Traffic Sign Based on Deep Convolution Neural Network

2020

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A neural network model based on deep learning is utilized to explore the traffic sign recognition (TSR) and expand the application of deep intelligent learning technology in the field of virtual reality (VR) image recognition, thereby assessing the road traffic safety risks and promoting the construction of intelligent transportation networks. First, a dual-path deep CNN (TDCNN) TSR model is built based on the convolutional neural network (CNN), and the cost function and recognition accuracy are selected as indicators to analyze the training results of the model. Second, the recurrent neural network (RNN) and long-short-term memory (LSTM) RNN are utilized to assess the road traffic safety risks, and the prediction and evaluation effects of them are compared. Finally, the changes in safety risks of road traffic accidents are analyzed based on the two key influencing factors of the number of road intersections and the speed of vehicles traveling. The results show that the learning rate of the network model and the number of hidden neurons in the fully-connected layer directly affect the training results, and there are differences in the choices between the early and late training periods. Compared with RNN, the LSTM network model has higher evaluation accuracy, and its corresponding root square error (RSE) is 0.36. The rational control of the number of intersections and the speed of roads traveled has a significant impact on improving the safety level and promoting road traffic efficiency. The VR image recognition algorithm and safety risk prediction method based on a neural network model positively affect the construction of an intelligent transport network.

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Section: Figure 4 Training Results Of Tdcnn Network Model Under Diffmentioning

confidence: 99%

Image Recognition and Safety Risk Assessment of Traffic Sign Based on Deep Convolution Neural Network

2020

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“…There are several most recent studies regarding this research topic. Henchri and Mtibaa [200] propose a two-stage approach for traffic sign detection. In the first stage, only the shape of the signs, which are circular or triangular, are detected and classified by using HOG [109] features and support vector machines (SVM).…”

Section: ) Detection Typesmentioning

confidence: 99%

Toward Performing Image Classification and Object Detection With Convolutional Neural Networks in Autonomous Driving Systems: A Survey

Turay

Vladimirova

2022

IEEE Access

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“…Since the deep network of the original deep structure of YOLO v3 is conducive to the detection of large targets, and the shallow structure is convenient for the detection of small targets because the shallow algorithm structure passes through small convolution layers [27], it lacks deep semantic features, contains less semantic information, and has weak feature representation ability, these features affect the detection of small targets, which depends on the shallow algorithm structure. In order to improve the feature extraction ability of the detection algorithm structure, this paper uses Inception architecture that can enrich the features of the shallow network for reference.…”

Section: Improvement Of Network Structurementioning

confidence: 99%

Intelligent Recognition of Traffic Signs Based on Improved YOLO v3 Algorithm

Yang

2022

Mobile Information Systems

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In recent years, assisted driving and autonomous driving technology have been paid more attention to by the public. Road sign recognition is of great practical significance for the realization of auto-driving technology. In the actual traffic environment, the traffic signs have the problems of small detectable volume, low resolution, unclear characteristics, and easy to be disturbed by the environment. In order to better realize road traffic sign recognition, this paper improves and optimizes the YOLO v3 network derived from YOLO v3 structure algorithm, enhances the data of the traffic signs by using color enhancement and other technologies, and improves the original FPN structure of the YOLO v3 network algorithm to 52 × 52. Then, the secondary sampling output characteristic diagram 108 in the YOLO v3 network is used × 108 solutions to solve these difficulties of picture size and image distortion. Use 5, 9, and 13 fixed-size pools in front of the surface of the control architecture, then the output characteristics are associated with the original characteristics of the picture so that inputs of different sizes can obtain the same output. Finally, we use the intermediate class K algorithm to group the TT100K landmark data set, reconsider the original network parameters, and compare the TT100K data set with the small target determination algorithm, such as YOLO v3 network model and improved YOLO v3 network model. The results show that compared with the traditional YOLO v3 algorithm, the optimized YOLO v3 road sign recognition algorithm has a significant improvement in sign recognition accuracy, sign recognition speed, and learning cost. When the change of FPS is very small, the recall rate and accuracy will be greatly improved. At the same time, compared with other small target detection algorithms, the improved YOLO v3 algorithm has more accurate and faster detection accuracy.

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Two‐stage traffic sign detection and recognition based on SVM and convolutional neural networks

Cited by 38 publications

References 35 publications

Image Recognition and Safety Risk Assessment of Traffic Sign Based on Deep Convolution Neural Network

Image Recognition and Safety Risk Assessment of Traffic Sign Based on Deep Convolution Neural Network

Toward Performing Image Classification and Object Detection With Convolutional Neural Networks in Autonomous Driving Systems: A Survey

Intelligent Recognition of Traffic Signs Based on Improved YOLO v3 Algorithm

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