Traffic Sign Detection using Clara and Yolo in Python

Valeja, Yogesh; Pathare, Shubham; Patel, Dipen; Pawar, Mohandas

doi:10.1109/icaccs51430.2021.9442065

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…27. Unknown or General Databases (Unknown): Consist of those datasets that do not have any certain information on the subject of traffic [22,42,64,73,86,88,93,99,100,110,119,122,141], or directly constitute general databases such as MSCOCO [63,73,99], KITTI [132], or those that are downloaded from repositories such as Kaggle [42].…”

Section: Rq2 [Datasetsmentioning

confidence: 99%

Traffic Sign Detection and Recognition Using YOLO Object Detection Algorithm: A Systematic Review

Flores-Calero,

Astudillo,

Guevara

et al. 2024

Mathematics

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Context: YOLO (You Look Only Once) is an algorithm based on deep neural networks with real-time object detection capabilities. This state-of-the-art technology is widely available, mainly due to its speed and precision. Since its conception, YOLO has been applied to detect and recognize traffic signs, pedestrians, traffic lights, vehicles, and so on. Objective: The goal of this research is to systematically analyze the YOLO object detection algorithm, applied to traffic sign detection and recognition systems, from five relevant aspects of this technology: applications, datasets, metrics, hardware, and challenges. Method: This study performs a systematic literature review (SLR) of studies on traffic sign detection and recognition using YOLO published in the years 2016–2022. Results: The search found 115 primary studies relevant to the goal of this research. After analyzing these investigations, the following relevant results were obtained. The most common applications of YOLO in this field are vehicular security and intelligent and autonomous vehicles. The majority of the sign datasets used to train, test, and validate YOLO-based systems are publicly available, with an emphasis on datasets from Germany and China. It has also been discovered that most works present sophisticated detection, classification, and processing speed metrics for traffic sign detection and recognition systems by using the different versions of YOLO. In addition, the most popular desktop data processing hardwares are Nvidia RTX 2080 and Titan Tesla V100 and, in the case of embedded or mobile GPU platforms, Jetson Xavier NX. Finally, seven relevant challenges that these systems face when operating in real road conditions have been identified. With this in mind, research has been reclassified to address these challenges in each case. Conclusions: This SLR is the most relevant and current work in the field of technology development applied to the detection and recognition of traffic signs using YOLO. In addition, insights are provided about future work that could be conducted to improve the field.

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Section: Rq2 [Datasetsmentioning

confidence: 99%

Traffic Sign Detection and Recognition Using YOLO Object Detection Algorithm: A Systematic Review

Flores-Calero,

Astudillo,

Guevara

et al. 2024

Mathematics

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“…Meanwhile, the single-stage TSR framework, such as You Only Look Once (YOLO) and the Single Shot Multibox Detector (SSD), eliminates the region-of-interest detection step and directly performs regression and classification of traffic signs [40,41]. A comparative performance analysis between YOLOv5 and SSD was performed for TSR [42].…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Convolutional Neural Network (CNN) Architecture for Traffic Sign Recognition in Urban Road Networks

2023

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Recognizing and classifying traffic signs is a challenging task that can significantly improve road safety. Deep neural networks have achieved impressive results in various applications, including object identification and automatic recognition of traffic signs. These deep neural network-based traffic sign recognition systems may have limitations in practical applications due to their computational requirements and resource consumption. To address this issue, this paper presents a lightweight neural network for traffic sign recognition that achieves high accuracy and precision with fewer trainable parameters. The proposed model is trained on the German Traffic Sign Recognition Benchmark (GTSRB) and Belgium Traffic Sign (BelgiumTS) datasets. Experimental results demonstrate that the proposed model has achieved 98.41% and 92.06% accuracy on GTSRB and BelgiumTS datasets, respectively, outperforming several state-of-the-art models such as GoogleNet, AlexNet, VGG16, VGG19, MobileNetv2, and ResNetv2. Furthermore, the proposed model outperformed these methods by margins ranging from 0.1 to 4.20 percentage point on the GTSRB dataset and by margins ranging from 9.33 to 33.18 percentage point on the BelgiumTS dataset.

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“…Successful results have been obtained in this study by Valeja et.al. by applying it to speed limit signs with a YOLO model trained on the CARLA simulator [7]. W. Yang and W. Zhang used a total of 15000 photographs dataset that includes Chinese warnings, speed limits, and prohibitive and instructing direction signs.…”

Section: Introductionmentioning

confidence: 99%

Solution of Real-Time Traffic Signs Detection Problem for Autonomous Vehicles by Using YOLOV4 And Haarcascade Algorithms

Ortatas

Çetin

2023

International Journal of Automotive Science and Technology

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Unmanned systems are increasingly used today to facilitate our daily lives and use time more efficiently. Therefore, this rapidly emerging and growing technology appears in every aspect of our lives with its various functions. Object recognition algorithms are one of the most important functions that we often encounter in these systems. Autonomous vehicle technologies are the latest and fastest-growing technology among unmanned systems. In this study, we investigate the success rates of two different algorithms for recognizing traffic signs and markings that can be used for partially or fully autonomous vehicles. In this study, two different solutions to the problem of recognizing the signs for fully autonomous and fully autonomous vehicles, respectively, were presented and the correct identification of the markers was evaluated. The work was performed in real-time. Two different con-cepts were used for these products. An enclosed space where an ideal lighting environment is provided for the evaluation of models should be visualized. In addition, for the general recognition of the models, the test procedures were performed with a dataset obtained from the users and it was computed for the general recognition. In addition, this study aims to provide a better understanding of the basic working principles, the differences between ma-chine learning and deep learning, and the contents of object recognition processes.

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Traffic Sign Detection using Clara and Yolo in Python

Cited by 7 publications

References 21 publications

Traffic Sign Detection and Recognition Using YOLO Object Detection Algorithm: A Systematic Review

Traffic Sign Detection and Recognition Using YOLO Object Detection Algorithm: A Systematic Review

A Lightweight Convolutional Neural Network (CNN) Architecture for Traffic Sign Recognition in Urban Road Networks

Solution of Real-Time Traffic Signs Detection Problem for Autonomous Vehicles by Using YOLOV4 And Haarcascade Algorithms

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