ArticleScientific reports2025
Fine-grained vehicle recognition under low light conditions using EfficientNet and image enhancement on LiDAR point cloud data.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- A study on intuitionistic fuzzy generating function using T-Norm, T-Conorm operators to enhance night-time images for autonomous driving system.Scientific reports · 2025Article
- Utilizing a deep neural network for robot semantic classification in indoor environments.Scientific reports · 2025Article
- Implementation of Integrated Smart Construction Monitoring System Based on Point Cloud Data and IoT Technique.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The detection and recognition of vehicles are crucial components of environmental perception in autonomous driving. Commonly used sensors include cameras and LiDAR. The performance of camera-based data collection is susceptible to environmental interference, whereas LiDAR, while unaffected by lighting conditions, can only achieve coarse-grained vehicle classification. This study introduces a novel method for fine-grained vehicle model recognition using LiDAR in low-light conditions. The approach involves collecting vehicle model data with LiDAR, performing projection transformation, enhancing the data using contrast limited adaptive histogram equalization combined with Gamma correction, and implementing vehicle model recognition based on EfficientNet. Experimental results demonstrate that the proposed method achieves an accuracy of 98.88% in fine-grained vehicle model recognition and an F1-score of 98.86%, showcasing excellent performance.
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.