ReviewSensors (Basel, Switzerland)2023
Recent Advances in Traffic Sign Recognition: Approaches and Datasets.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- CW-DETR: An Efficient Detection Transformer for Traffic Signs in Complex Weather.Sensors (Basel, Switzerland) · 2026Article
- Traffic sign dataset for connected and automated vehicle operations in rural areas.Data in brief · 2025Article
- Analysis of AWS Rekognition and Azure Custom Vision Performance in Parking Sign Recognition.Sensors (Basel, Switzerland) · 2025Article
- TSD-Net: A Traffic Sign Detection Network Addressing Insufficient Perception Resolution and Complex Background.Sensors (Basel, Switzerland) · 2025Article
- Efficient traffic sign recognition using YOLO for intelligent transport systems.Scientific reports · 2025Article
- A photodetector for red and green with balanced negative and positive photocurrent for imaging is realized.Scientific reports · 2025Article
- A Vision-Language Model-Based Traffic Sign Detection Method for High-Resolution Drone Images: A Case Study in Guyuan, China.Sensors (Basel, Switzerland) · 2024Article
- Traffic Sign Recognition Using Multi-Task Deep Learning for Self-Driving Vehicles.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Autonomous vehicles have become a topic of interest in recent times due to the rapid advancement of automobile and computer vision technology. The ability of autonomous vehicles to drive safely and efficiently relies heavily on their ability to accurately recognize traffic signs. This makes traffic sign recognition a critical component of autonomous driving systems. To address this challenge, researchers have been exploring various approaches to traffic sign recognition, including machine learning and deep learning. Despite these efforts, the variability of traffic signs across different geographical regions, complex background scenes, and changes in illumination still poses significant challenges to the development of reliable traffic sign recognition systems. This paper provides a comprehensive overview of the latest advancements in the field of traffic sign recognition, covering various key areas, including preprocessing techniques, feature extraction methods, classification techniques, datasets, and performance evaluation. The paper also delves into the commonly used traffic sign recognition datasets and their associated challenges. Additionally, this paper sheds light on the limitations and future research prospects of traffic sign recognition.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.