Traffic Sign Detection and Recognition Using YOLO Object Detection Algorithm: A Systematic Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Traffic Signs
2.2. YOLO Object Detection Algorithm
2.3. State of the Art
2.4. Systematic Literature Review Methodology
2.5. Research Questions
2.6. Search Strategy
2.6.1. Databases of Digital Library
2.6.2. Timeframe of Study
2.6.3. Keywords
2.6.4. Inclusion and Exclusion Criteria
- Studies must evaluate traffic sign detection or recognition using the YOLO object detection algorithm.
- Only studies published between 2016 and 2022 are considered.
- The study should be published in a peer-reviewed journal or conference proceedings.
- Preference is given to documents categorized as “Journal” or “Conference” articles.
- The study must be in English.
- Studies that do not utilize the YOLO object detection algorithm for traffic sign detection or recognition.
- Research not focused on traffic sign detection or recognition.
- Publications outside the 2016–2022 timeframe.
- Non-peer-reviewed articles and documents.
- Studies published in languages other than English.
2.6.5. Study Selection
2.7. Data Extraction
2.8. Data Synthesis
3. Results Based on RQs
3.1. RQ1 [Applications]: What Are the Main Applications of Traffic Sign Detection and Recognition Using YOLO?
3.2. RQ2 [Datasets]: What Traffic Sign Datasets Are Used to Train, Validate, and Test These Systems?
- The German Traffic Sign Detection Benchmark (GTSDB) and the German Traffic Sign Recognition Benchmark (GTSRB): The GTSDB and GTSRB datasets are two popular resources for traffic sign recognition research. They contain high-quality images of various traffic signs taken in real-world scenarios in Germany. The images cover a wide range of scenery, times of day, and weather conditions, making them suitable for testing the robustness of recognition algorithms. The GTSDB dataset consists of 900 images, split into 600 for training and 300 for validation. The GTSRB dataset is larger, with more than 50,000 images of 43 classes of traffic signs, such as speed limit signs, stop signs, yield signs, and others. Images are also annotated with bounding boxes and class labels. Both datasets are publicly available and have been used in several benchmarking studies [7,13,37,38,40,46,47,50,52,53,55,58,59,60,63,64,65,66,68,69,70,71,72,77,80,82,92,96,101,103,104,106,109,112,114,115,117,118,125,127,129,130,136,137].
- Tsinghua Tencent 100K (TT100K): The TT100K dataset is a large-scale traffic sign benchmark created by Tsinghua University and Tencent. It consists of 100,000 images from Tencent Street View panoramas, which contain 30,000 traffic sign instances. The images vary in lighting and weather conditions, and each traffic sign is annotated with its class label, bounding box, and pixel mask. The dataset is suitable for traffic sign detection and classification tasks in realistic scenarios. The TT100K dataset is publicly available and can be used for both traffic sign detection and classification tasks [35,38,41,45,51,65,67,80,84,94,95,102,105,108,109,111,113,121,124,128,131,133,134,135].
- Chinese Traffic Sign Dataset (CTSDB y CCTSDB): The CTSDB and CCTSDB datasets are two large-scale collections of traffic sign images for CV research. The CTSDB dataset consists of 10,000 images captured from different scenes and angles, covering a variety of types and shapes of traffic signs. The CCTSDB dataset is an extension of the CTSD dataset, with more than 20,000 images that contain approximately 40,000 traffic signs. The CCTSDB dataset also includes more challenging scenarios, such as occlusion, illumination variation, and scale change [8,18,36,39,60,65,68,81,82,83,85,87,108,116,130,132,135,136,138].
- Belgium Traffic Sign Detection Benchmark and Belgium Traffic Sign Classification Benchmark (BTSDB y BTCDB): The BTSDB dataset, specifically designed for traffic sign detection in Belgium, comprises a total of 7095 images. These images are further divided into 4575 training images and 2520 testing images. The dataset encompasses a diverse range of image sizes, spanning from 11 × 10 pixels to 562 × 438 pixels. The Belgium Traffic Sign Classification Benchmark is a dataset of traffic sign images collected from eight cameras mounted on a van. The dataset contains 62 types of traffic signs and is divided into training and testing sets. The dataset is useful for evaluating traffic sign recognition algorithms, which are essential for intelligent transport systems and autonomous driving. The dataset also provides annotations, background images, and test sequences for further analysis [55,78,101,123].
- Malaysian Traffic Sign Dataset (MTSD): The MTSD includes a variety of traffic sign scenes to be used in traffic sign detection, having 1000 images with different resolutions (FHD pixels; 4K-UHD pixels; UHD+ pixels). It also has 2056 images of traffic signs, divided into five categories, for recognition [11,118].
- Korea Traffic Sign Dataset (KTSD): This dataset has been used to train and test various deep learning architectures, such as YOLOv3 [57], to detect three different classes of traffic signs: prohibitory, mandatory, and danger. The KTSD contains 3300 images of various traffic signs, captured from different roads throughout South Korea. These images feature traffic signs of varying sizes, providing a diverse and comprehensive dataset for traffic sign detection and recognition research [57,59,64].
- Berkley Deep Drive (BDD100K): The Berkeley DeepDrive (BDD) project has released a large-scale and diverse driving video dataset called BDD100K. It contains 100,000 videos with rich annotations to evaluate the progress of image recognition algorithms on autonomous driving. The dataset is available for research purposes and can be downloaded from the BDD website (https://bdd-data.berkeley.edu/, accessed on 12 April 2023). The images in the dataset are divided into two sets: one for training and one for validation. The training set contains 70% of the images, while the validation set contains the remaining 30% [55,90].
- Thai (Thailand) Traffic Sign Dataset (TTSD): The data collection process takes place in the rural areas of Maha Sarakham and Kalasin Provinces within the Kingdom of Thailand. It encompasses 50 distinct classes of traffic signs, each comprising 200 unique instances, resulting in a comprehensive sign dataset that comprises a total of 9357 images [101,126].
- Swedish Traffic Sign Dataset (STSD): This public sign dataset comprises 20,000 images, with 20% of them labeled. Additionally, it contains 3488 traffic signs from Sweden [104].
- DFG Traffic Sign Dataset (DFG): The DFG dataset comprises approximately 7000 traffic sign images captured from highways in Slovenia. These images have a resolution of pixels. To facilitate training and evaluation, the dataset is divided into two subsets, with 5254 images designated for training and the remaining 1703 images for validation. The dataset features a total of 13,239 meticulously annotated instances in the form of polygons, each spanning over 30 pixels. Additionally, there are 4359 instances with less precise annotations represented as bounding boxes, measuring less than 30 pixels in width [12].
- Taiwan Traffic Sign Dataset (TWTSD: The TWTSD dataset comprises 900 prohibitory signs from Taiwan with a resolution of pixels. The training and validation subsets are divided into 70% and 30%, respectively [75].
- Taiwan Traffic Sign (TWSintetic): The Taiwan Traffic Sign (TWSintetic) dataset is a collection of traffic signs from Taiwan, consisting of 900 images, and it has been expanded using generative adversarial network techniques [9].
- Belgium Traffic Signs (KUL): The KUL dataset encompasses over 10,000 images of traffic signs from the Flanders region in Belgium, categorized into more than 100 distinct classes [89].
- Chinese Traffic Sign Detection Benchmark (CSUST): The CSUST dataset comprises over 15,000 images and is continuously updated to incorporate new data [8].
- Foggy Road Image Database (FRIDA): The Foggy Road Image Database (FRIDA) contains 90 synthetic images from 18 scenes depicting various urban road settings. In contrast, FRIDA2 offers an extended collection, with 330 images derived from 66 road scenes. For each clear image, there are corresponding counterparts featuring four levels of fog and a depth map. The fog variations encompass uniform fog, heterogeneous fog, foggy haze, and heterogeneous foggy haze [71,114].
- Foggy ROad Sign Images (FROSI): The FROSI is a database of synthetic images easily usable to evaluate the performance of road sign detectors in a systematic way in foggy conditions. The database contains a set of 504 original images with 1620 road signs (speed and stop signs, pedestrian crossing) placed at various ranges, with ground truth [71,114].
- MarcTR: This dataset contains seven traffic sign classes, collected by using a ZED stereo camera mounted on top of Racecar mini autonomous car [79].
- Turkey Traffic Sign Dataset: The Turkey Traffic Sign Dataset is an essential resource for the development of traffic and road safety technologies, specifically tailored for the Turkish environment. It comprises approximately 2500 images, including a diverse range of traffic signs, pedestrians, cyclists, and vehicles, all captured under real-world conditions in Turkey [77].
- Vietnamese Traffic Sign Dataset: This comprehensive dataset encompasses 144 classes of traffic signs found in Vietnam, categorized into four distinct groups for ease of analysis and application. These include 40 prohibitory or restrictive signs, 47 warning signs, 10 mandatory signs, and 47 indication signs, providing a detailed overview of the country’s traffic sign system [76].
- Croatia Traffic Sign Dataset: This dataset consists of 28 video sequences at 30 FPS with a resolution of pixels. They were taken in the urban traffic of the city of Osijek, Croatia [10].
- Mexican Traffic Sign Dataset: The dataset consists of 1284 RGB images, featuring a total of 1426 traffic signs categorized into 11 distinct classes. These images capture traffic signs from a variety of perspectives, sizes, and lighting conditions, ensuring a diverse and comprehensive collection. The traffic sign images were sourced from a range of locations including avenues, roadways, parks, green areas, parking lots, and malls in Ciudad Juárez, Chihuahua, and Monterrey, Nuevo Leon, Mexico, providing a broad representation of the region’s signage [43].
- WHUTCTSD: It is a more recent dataset with five categories of Chinese traffic signs, including prohibitory signs, guide signs, mandatory signs, danger warning signs, and tourist signs. Data were collected by a camera at a pixel resolution during different time periods. It consists of 2700 images, which were extracted from videos collected in Wuhan, Hubei, China [62].
- Bangladesh Road Sign 2021 (BDRS2021): This dataset consists of 16 classes. Each class consists of 168 images of Bangladesh road signs [69], offering a rich source of data that capture the specific traffic sign environment of Bangladesh, including its urban, rural, and varied geographical landscapes.
- New Zealand Traffic Sign 3K (NZ-TS3K): This dataset is a specialized collection focused on traffic sign recognition in New Zealand [70]. It features over 3000 images, showcasing a wide array of traffic signs commonly found across the country. These images are captured in high resolution (1080 by 1440 pixels), providing clear and detailed visuals essential for accurate recognition and analysis. The dataset is categorized into multiple classes, each representing a different type of traffic sign. These include Stop (236 samples), Keep Left (536 samples), Road Diverges (505 samples), Road Bump (619 samples), Crosswalk Ahead (636 samples), Give Way at Roundabout (533 samples), and Roundabout Ahead (480 samples), offering a diverse range of signs commonly seen on Auckland’s roads.
- Mapillary Traffic Sign Dataset (MapiTSD): The Mapillary Traffic Sign Dataset is an expansive and diverse collection of traffic sign images, sourced globally from the Mapillary platform’s extensive street-level imagery. It features millions of images from various countries, each annotated with automatically detected traffic signs. This dataset is characterized by its wide-ranging geographic coverage and diversity in environmental conditions, including different lighting, weather, and sign types. Continuously updated, it provides a valuable up-to-date resource for training and validating traffic sign recognition algorithms [38].
- Specialized Research Datasets: These datasets consist of traffic sign data compiled by various authors. Generally, they lack detailed public information and are not openly accessible. This category includes datasets from a variety of countries: South Korea [91,103], India [49,99], Malaysia [97], Indonesia [98], Slovenia [54], Argentina [139], Taiwan [74,107,140], Bangladesh [69], and Canada [61]. Each dataset is tailored to its respective country, reflecting the specific traffic signs and road conditions found there.
- 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].
3.3. RQ3 [Metrics]: What Metrics Are Used to Measure the Quality of Object Detection in the Context of Traffic Sign Detection and Recognition Using YOLO?
3.4. Comparing Metrics among Different Versions of YOLO
3.5. RQ4 [Hardware]: What Hardware Is Used to Implement Traffic Sign Recognition and Detection Systems Based on YOLO?
3.7. Discussion
3.8. Possible Threats to SLR Validation
3.9. Construct Validity Threat
3.9.1. The Influence of Version Bias on Metrics
3.9.2. Geographic Diversity in Sign Datasets
3.9.3. Hardware Discrepancies
3.9.4. Limitations of Statistical Metrics
3.9.5. Threats to Internal Validation
3.9.6. Threats to External Validation
3.9.7. Threats to the Validation of the Conclusions
3.10. Future Research Directions
- What is the performance of traffic sign detection and recognition systems under extreme weather conditions?
- How could datasets for the development of traffic sign detection and recognition systems be standardized?
- What is the best object detector to be used in traffic sign detection and recognition systems?
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
WHO | World Health Organization |
RTA | Road Traffic Accidents |
ADAS | Advanced Driver Assistance Systems |
DL | Deep Learning |
CV | Computer Vision |
YOLO | You Look Only Once |
GPU | Graphics Processing Unit |
SLR | Systematic Literature Review |
RQ | Research question |
FPS | Frames per second |
ACC | Accuracy |
AP | Average Precision |
mAP | Mean Average Precision |
F1 | F1 Score |
IoU | Intersection over Union |
WoS | Web of Science |
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Databases | Search Strings | Results |
---|---|---|
IEEE | ((“All Metadata”: Traffic Sign) AND ((“All Metadata”:Detection) OR (“All Metadata”:Recognition) OR (“All Metadata”:Identification)) AND (“All Metadata”: Object Detection) OR (“All Metadata”:YOLO)) | 2722 |
Springer | ‘Object AND Detection AND “Traffic Sign” AND (Detection OR Recognition OR Identification OR YOLO)’ | 1852 |
MDPI | All Fields: Traffic Sign Detection OR All Fields: Traffic Sign Recognition OR All Fields: Traffic Sign Identification AND Keywords: You Only Look Once OR Keywords: Object Detection | 498 |
Hindawi P.G. | (“Traffic Sign” AND (“Detection” OR “Recognition” OR “Identification”)) AND (“YOLO”) | 16 |
Science Direct | (Traffic Sign OR Traffic Sign Detection OR Traffic Sign Recognition OR Traffic Sign Identification OR Object Detection) AND (YOLO OR You Only Look Once) | 80,650 |
Wiley | “Traffic Sign OR Detection OR Recognition OR Identification” anywhere and “YOLO OR Object Detection” anywhere | 160,803 |
Sage | Traffic Sign OR Detection OR Recognition OR Identification AND Object Detection OR YOLO | 72,941 |
Taylor & Francis | [All: traffic] AND [[All: sign] OR [All: detection] OR [All: recognition] OR [All: identification]] AND [[All: object] OR [All: detection] OR [All: yolo]] | 135,385 |
PLOS | ((((everything: “Traffic Sign”) AND everything:Detection) OR everything:Identification) OR everything:YOLO) OR everything: “Object Detection” | 140,023 |
Order | Country of Origin | Name of Dataset | Number of Categories | Number of Classes | Number of Images | Number of Traffic Signs | Number of Referenced Articles | Percentage (%) |
---|---|---|---|---|---|---|---|---|
1 | Germany | GTSRB and | +3 | 43 | 52,740 | 52,740 | 44 | 27.33 |
GTSDB | 900 | 910 | ||||||
2 | China | TT100K | 3 | 130 | 100,000 | 30,000 | 26 | 16.15 |
3 | China | CTSDB | 10,000 | 21 | 13.04 | |||
CCTSDB | 3 | 21 | ∼20,000 | ∼40,000 | ||||
4 | Belgium | BTSDB and | 3 | 62 | 17,000 | 4627 | 6 | 3.73 |
BTSCB | 7125 | 7125 | ||||||
5 | South Korea | KTSD | - | - | 3300 | - | 3 | 1.86 |
6 | Malaysia | MTSD | 5 | 66 | 1000 | 2 | 1.86 | |
2056 | 2056 | |||||||
7 | USA | BDD1OOK | - | - | 100,000 | - | 2 | 1.86 |
8 | Thailand | TTSD | - | 50 | 9357 | - | 2 | 1.86 |
9 | France | FRIDA and | - | 90 | - | 2 | 1.86 | |
FRIDA2 | - | 300 | ||||||
10 | France | FROSI | - | 4 | 504 | 1620 | 2 | 1.86 |
11 | Sweden | STSD | - | 7 | 20,000 | 3488 | 2 | 1.86 |
12 | Slovenia | DFG | +3 | +200 | 7000 | 13,239 | 1 | 0.62 |
4359 | ||||||||
13 | Taiwan | TWTSD | - | - | 900 | - | 1 | 0.62 |
14 | Taiwan | TWynthetic | - | 3 | - | 900 | 1 | 0.62 |
15 | Belgium | KUL | - | +100 | +10,000 | - | 1 | 0.62 |
16 | China | CSUST | - | - | 15,000 | - | 1 | 0.62 |
17 | MarcTR | - | 7 | 3564 | 3564 | 1 | 0.62 | |
18 | Turkey | - | 22 | 2500 | 1 | 0.62 | ||
19 | Vietnam | - | 4 | 144 | 5000 | 5704 | 1 | 0.62 |
20 | Croatia | - | - | 11 | 5567 | 6751 | 1 | 0.62 |
21 | Mexico | - | 3 | 11 | 1284 | 1426 | 1 | 0.62 |
22 | China | WHUTCTSD | 5 | - | 2700 | 4009 | 1 | 0.62 |
23 | Bangladesh | BDRS2021 | 4 | 16 | 2688 | - | 1 | 0.62 |
24 | New Zealand | NZ-TS3K | 3 | 7 | 3436 | 3545 | 1 | 0.62 |
25 | AW 1 | MapiTSD | - | 300 | 100,000 | 320,000 | 1 | 0.62 |
26 | SW 2 | Own | - | - | - | - | 14 | 8.70 |
27 | SW | Unknown | - | - | - | 21 | 13.0 | |
Total | 161 | 100.00 |
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Flores-Calero, M.; Astudillo, C.A.; Guevara, D.; Maza, J.; Lita, B.S.; Defaz, B.; Ante, J.S.; Zabala-Blanco, D.; Armingol Moreno, J.M. Traffic Sign Detection and Recognition Using YOLO Object Detection Algorithm: A Systematic Review. Mathematics 2024, 12, 297. https://doi.org/10.3390/math12020297
Flores-Calero M, Astudillo CA, Guevara D, Maza J, Lita BS, Defaz B, Ante JS, Zabala-Blanco D, Armingol Moreno JM. Traffic Sign Detection and Recognition Using YOLO Object Detection Algorithm: A Systematic Review. Mathematics. 2024; 12(2):297. https://doi.org/10.3390/math12020297
Chicago/Turabian StyleFlores-Calero, Marco, César A. Astudillo, Diego Guevara, Jessica Maza, Bryan S. Lita, Bryan Defaz, Juan S. Ante, David Zabala-Blanco, and José María Armingol Moreno. 2024. "Traffic Sign Detection and Recognition Using YOLO Object Detection Algorithm: A Systematic Review" Mathematics 12, no. 2: 297. https://doi.org/10.3390/math12020297