Fine-grained vehicle recognition under low light conditions using EfficientNet and image enhancement on LiDAR point cloud data DOI Creative Commons

Guanqiang Ruan,

Tao Hu, Caichang Ding

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 8, 2025

The detection and recognition of vehicles are crucial components environmental perception in autonomous driving. Commonly used sensors include cameras LiDAR. performance camera-based data collection is susceptible to interference, whereas LiDAR, while unaffected by lighting conditions, can only achieve coarse-grained vehicle classification. This study introduces a novel method for fine-grained model using LiDAR low-light conditions. approach involves collecting with performing projection transformation, enhancing the contrast limited adaptive histogram equalization combined Gamma correction, implementing based on EfficientNet. Experimental results demonstrate that proposed achieves an accuracy 98.88% F1-score 98.86%, showcasing excellent performance.

Язык: Английский

Fine-grained vehicle recognition under low light conditions using EfficientNet and image enhancement on LiDAR point cloud data DOI Creative Commons

Guanqiang Ruan,

Tao Hu, Caichang Ding

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 8, 2025

The detection and recognition of vehicles are crucial components environmental perception in autonomous driving. Commonly used sensors include cameras LiDAR. performance camera-based data collection is susceptible to interference, whereas LiDAR, while unaffected by lighting conditions, can only achieve coarse-grained vehicle classification. This study introduces a novel method for fine-grained model using LiDAR low-light conditions. approach involves collecting with performing projection transformation, enhancing the contrast limited adaptive histogram equalization combined Gamma correction, implementing based on EfficientNet. Experimental results demonstrate that proposed achieves an accuracy 98.88% F1-score 98.86%, showcasing excellent performance.

Язык: Английский

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