Autonomous Traffic Sign (ATSR) Detection and Recognition using Deep CNN

Alghmgham, Danyah A.; Latif, Ghazanfar; Alghazo, Jaafar; Alzubaidi, Loay

doi:10.1016/j.procs.2019.12.108

Cited by 64 publications

(20 citation statements)

References 11 publications

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“…Song et al [38] proposed an efficient convolutional neural network (CNN), which can significantly reduce the redundancy, reduce the parameters, and improve the speed of the networks. In the study by Alghmgham et al [39], automatic Arabic traffic sign (AATS) recognition system was designed using convolutional neural networks (CNN). Arcos-García et al [40] presented a two-stage traffic sign recognition system with high efficiency.…”

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confidence: 99%

A Novel Neural Network Model for Traffic Sign Detection and Recognition under Extreme Conditions

Wan

Gao

et al. 2021

Journal of Sensors

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Traffic sign detection is extremely important in autonomous driving and transportation safety systems. However, the accurate detection of traffic signs remains challenging, especially under extreme conditions. This paper proposes a novel model called Traffic Sign Yolo (TS-Yolo) based on the convolutional neural network to improve the detection and recognition accuracy of traffic signs, especially under low visibility and extremely restricted vision conditions. A copy-and-paste data augmentation method was used to build a large number of new samples based on existing traffic-sign datasets. Based on You Only Look Once (YoloV5), the mixed depth-wise convolution (MixConv) was employed to mix different kernel sizes in a single convolution operation, so that different patterns with various resolutions can be captured. Furthermore, the attentional feature fusion (AFF) module was integrated to fuse the features based on attention from same-layer to cross-layer scenarios, including short and long skip connections, and even performing the initial fusion with itself. The experimental results demonstrated that, using the YoloV5 dataset with augmentation, the precision was 71.92, which was increased by 34.56 compared with the data without augmentation, and the mean average precision mAP_0.5 was 80.05, which was increased by 33.11 compared with the data without augmentation. When MixConv and AFF were applied to the TS-Yolo model, the precision was 74.53 and 2.61 higher than that with data augmentation only, and the value of mAP_0.5 was 83.73 and 3.68 higher than that based on the YoloV5 dataset with augmentation only. Overall, the performance of the proposed method was competitive with the latest traffic sign detection approaches.

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confidence: 99%

A Novel Neural Network Model for Traffic Sign Detection and Recognition under Extreme Conditions

Wan

Gao

et al. 2021

Journal of Sensors

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“…Then, the images are recognized by a CNN, and the large-scale structure of the information contained in traffic sign images is obtained using a hierarchical meaning detection method based on graphic models. On the other hand, two CNN architectures are presented in this work [25]; the first one contains 8 layers, and after an enhancement, the second one contains 6 layers; it is a lightweight architecture. Both models are tested on a database of road signs in Saudi Arabia.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Model for Traffic Sign Classification Based on Enhanced LeNet‐5 Network

2021

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For several years, much research has focused on the importance of traffic sign recognition systems, which have played a very important role in road safety. Researchers have exploited the techniques of machine learning, deep learning, and image processing to carry out their research successfully. The new and recent research on road sign classification and recognition systems is the result of the use of deep learning-based architectures such as the convolutional neural network (CNN) architectures. In this research work, the goal was to achieve a CNN model that is lightweight and easily implemented for an embedded application and with excellent classification accuracy. We choose to work with an improved network LeNet-5 model for the classification of road signs. We trained our model network on the German Traffic Sign Recognition Benchmark (GTSRB) database and also on the Belgian Traffic Sign Data Set (BTSD), and it gave good results compared to other models tested by us and others tested by different researchers. The accuracy was 99.84% on GTSRB and 98.37% on BTSD. The lightness and the reduced number of parameters of our model (0.38 million) based on the enhanced LeNet-5 network pushed us to test our model for an embedded application using a webcam. The results we found are efficient, which emphasize the effectiveness of our method.

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“…Finally, the traffic sign is classified by the Softmax classifier to complete the detection of the traffic sign. Alghmgham et al [83] designed a deep-learning-based architecture and applied it in the real-time traffic sign classification. The proposed architecture in [83] consists of two convolutional layers, two max-pooling layers, one dropout layer and three dense layers.…”

Section: Traffic Signs and Lights Recognitionmentioning

confidence: 99%

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

Chen

et al. 2020

Applied Sciences

118

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Self-driving cars are a hot research topic in science and technology, which has a great influence on social and economic development. Deep learning is one of the current key areas in the field of artificial intelligence research. It has been widely applied in image processing, natural language understanding, and so on. In recent years, more and more deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. This paper presents a review of recent research on theories and applications of deep learning for self-driving cars. This survey provides a detailed explanation of the developments of self-driving cars and summarizes the applications of deep learning methods in the field of self-driving cars. Then the main problems in self-driving cars and their solutions based on deep learning methods are analyzed, such as obstacle detection, scene recognition, lane detection, navigation and path planning. In addition, the details of some representative approaches for self-driving cars using deep learning methods are summarized. Finally, the future challenges in the applications of deep learning for self-driving cars are given out.

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Autonomous Traffic Sign (ATSR) Detection and Recognition using Deep CNN

Cited by 64 publications

References 11 publications

A Novel Neural Network Model for Traffic Sign Detection and Recognition under Extreme Conditions

A Novel Neural Network Model for Traffic Sign Detection and Recognition under Extreme Conditions

A Lightweight Model for Traffic Sign Classification Based on Enhanced LeNet‐5 Network

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

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