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

Abstract

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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MSGC-YOLO: An Improved Lightweight Traffic Sign Detection Model under Snow Conditions

“…With traffic sign recognition technology, as an important part of the intelligent transportation system (ITS) [1], the accuracy of its recognition is directly related to whether the driving system is safe and reliable. Traffic sign recognition (TSR) [2] is mainly used to collect and recognize the traffic signs on the road (e.g., speed limit, no left turn, and high limit) during the process of automobile driving, and the main goal of TSR is to make the driving system able to automatically recognize the various traffic signs on the road, and to provide the driver with a safe, reliable, and reliable driving system through the use of computerized image processing and pattern recognition and other related technologies. The main goal of TSR is to enable the driving system to automatically recognize all kinds of traffic signs on the road by using computer image processing and pattern recognition technologies, so as to provide timely and accurate traffic information for the driver and the vehicle system.…”