Intensive Survey About Road Traffic Signs Preprocessing, Detection and Recognition

Almuhajri, Mrouj; Suen, Ching Y.

doi:10.1007/978-3-030-36365-9_23

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…Among the multitude of characteristics, two stand out, namely shape and color, from which traffic signs can be grouped into three types: prohibitive, preventive, and informative [19,20]. Prohibitive (or regulatory) signs inform the driver of the restrictions he must comply with; they are often circular and red in color.…”

Section: Traffic Signsmentioning

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: Traffic Signsmentioning

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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“…Although recognizing text in natural scenes has been an ongoing research topic for decades, many studies focused on the consistent appearance of text like in traffic signs [3], and license plates [4]. However, other signs like shop signboards have gotten less attention because of their complex visual appearance.…”

Section: Introductionmentioning

confidence: 99%

AI Based Approach for Shop Classification and a Comparative Study with Human

Almuhajri¹,

Suen²

2022

AAIML

Self Cite

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The rapid advancements in artificial intelligence algorithms have sharpened the focus on street signs due to their prevalence. Some street signs have consistent shapes and pre-defined colors and fonts, such as traffic signs while others are characterized by their visual variability like shop signboards. This variations create a complicated challenge for AI-based systems to classify them. In this paper, the annotation of the ShoS dataset were extended to include more attributes for shop classification. Then, two classifiers were trained and tested utilizing the extended ShoS dataset. SVM showed great performance as its F1-score reached 89.33\%. The classification performance was compared with human performance, and the results showed that our classifier excelled over human performance by about 15\%. The results were discussed, so the factors that affect classification were provided for further enhancement.

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A Data-Centric Approach to Investigate the Feasibility of Utilizing Animal Medical Data as a Solution for Human Medical Data Scarcity

Al-Qudah,

Suen

2024

IEEE Access

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Intensive Survey About Road Traffic Signs Preprocessing, Detection and Recognition

Cited by 4 publications

References 29 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

AI Based Approach for Shop Classification and a Comparative Study with Human

A Data-Centric Approach to Investigate the Feasibility of Utilizing Animal Medical Data as a Solution for Human Medical Data Scarcity

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