Smart City Gnosys
Smart city article details
| Title | Recent Advancement In Small Traffic Sign Detection: Approaches And Dataset |
|---|---|
| ID_Doc | 44538 |
| Authors | Suresha R.; Manohar N.; Ajay Kumar G.; Rohit Singh M. |
| Year | 2024 |
| Published | IEEE Access, 12 |
| DOI | http://dx.doi.org/10.1109/ACCESS.2024.3514692 |
| Abstract | Detecting small traffic signs is essential for autonomous vehicles (AV) to operate safely, adhere to traffic regulations, navigate effectively, maintain situational awareness, adapt to local conditions, and overcome the technical challenges associated with diverse environments. As AV technology continues to evolve, improving the accuracy and reliability of small sign detection remains a crucial area of research and development. Enforcing small traffic sign detection systems (TSDS) is essential for road safety, ensuring drivers know important information, warnings, and regulations aligned with smart cities, enhancing traffic management, and optimizing signal timings. Over the past ten years, many deep-learning techniques have been reported for TSDS to identify tiny traffic regions. This review comprehensively examines the performance of state-of-the-art deep learning models, including YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), and various RCNN (Region-based Convolutional Neural Network) variants, assessing their strengths and weaknesses for small traffic sign detection through detailed tables and bar graphs. Additionally, the review examines key evaluation metrics used for TSDS and explores standard benchmark datasets like TT100k, GTSDB, CCTSD, STS, and DFG. It emphasizes the dataset's attributes and complex factors affecting detection performance. The analysis focuses on how various deep learning models perform on these standard datasets, presenting the results in tables and comparative bar graphs. Finally, the review discusses current challenges in TSDS technology and proposes recommendations for future development of driver-aid systems. This comprehensive analysis aims to guide the design of future computer vision systems for small traffic sign detection. © 2013 IEEE. |
| Author Keywords | attention model; dataset; Recent advancement; semantic features; small traffic sign detection |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
11302  | 0.917 | Srivastava V.; Mishra S.; Gupta N. | Automatic Detection And Categorization Of Road Traffic Signs Using A Knowledge-Assisted Method | Procedia Computer Science, 218 (2022) |
| 32396 | 0.892 | Hegde S.K.; Dharmalingam R.; Kannan S. | Intelligent German Traffic Sign And Road Barrier Assist For Autonomous Driving In Smart Cities | Multimedia Tools and Applications, 83, 22 (2024) |
| 30032 | 0.891 | Dhawan K.; Srinivasa Perumal R.; Nadesh R.K. | Identification Of Traffic Signs For Advanced Driving Assistance Systems In Smart Cities Using Deep Learning | Multimedia Tools and Applications, 82, 17 (2023) |
| 44408 | 0.888 | Khalifa A.A.; Alayed W.M.; Elbadawy H.M.; Sadek R.A. | Real-Time Navigation Roads: Lightweight And Efficient Convolutional Neural Network (Le-Cnn) For Arabic Traffic Sign Recognition In Intelligent Transportation Systems (Its) | Applied Sciences (Switzerland), 14, 9 (2024) |
| 40775 | 0.887 | Malhotra R.; Saanidhi; Gupta D. | Optimizing Cnn Architecture Using Genetic Algorithm For Classification Of Traffic Signs In Real Time | Lecture Notes in Networks and Systems, 473 (2023) |
| 45887 | 0.887 | Wang C. | Research On Traffic Sign Recognition Algorithms For Autonomous Vehicles In Smart City Traffic Systems | Proceedings of SPIE - The International Society for Optical Engineering, 13256 (2024) |
| 58662 | 0.885 | Ayachi R.; Afif M.; Said Y.; Abdelali A.B. | Traffic Sign Detection For Smart Public Transport Vehicles: Cascading Convolutional Autoencoder With Convolutional Neural Network | Artificial Intelligence for Smart Cities and Villages: Advanced Technologies, Development, and Challenges (2022) |
| 58661 | 0.885 | Ayachi R.; Afif M.; Said Y.; Abdelali A.B. | Traffic Sign Detection For Green Smart Public Transportation Vehicles Based On Light Neural Network Model | Green Energy and Technology (2022) |
| 17855 | 0.88 | Faraji P.H.; Tohidypour H.R.; Wang Y.; Nasiopoulos P.; Ren S.; Rizvi A.; Feng C.; Pourazad M.T.; Leung V.C.M. | Deep Learning Based Street Parking Sign Detection And Classification For Smart Cities | GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good (2021) |
| 2473 | 0.88 | Smitha Shekar B.; Harish G. | A Machine Learning Model For Detection And Recognition Of Traffic Signs | 2021 International Conference on Intelligent Technologies, CONIT 2021 (2021) |