ArticleEntropy (Basel, Switzerland)2022
Visual Recognition of Traffic Signs in Natural Scenes Based on Improved RetinaNet.
Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed.
- An Improved YOLO11n-Based Algorithm for Road Sign Detection.Sensors (Basel, Switzerland) · 2026Article
- POP-YOLOv8: an object detection framework for partially occluded pedestrians in nighttime traffic environments.Scientific reports · 2026Article
- Enhanced multi-scale trademark element detection using the improved DETR.Scientific reports · 2024Article
- Improving real-time object detection in Internet-of-Things smart city traffic with YOLOv8-DSAF method.Scientific reports · 2024Article
- Comparison of RetinaNet-Based Single-Target Cascading and Multi-Target Detection Models for Administrative Regions in Network Map Pictures.Sensors (Basel, Switzerland) · 2022Article
- A Small Network MicronNet-BF of Traffic Sign Classification.Computational intelligence and neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Aiming at recognizing small proportion, blurred and complex traffic sign in natural scenes, a traffic sign detection method based on RetinaNet-NeXt is proposed. First, to ensure the quality of dataset, the data were cleaned and enhanced to denoise. Secondly, a novel backbone network ResNeXt was employed to improve the detection accuracy and effection of RetinaNet. Finally, transfer learning and group normalization were adopted to accelerate our network training. Experimental results show that the
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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.