YOLOv10-DRIVEN ENHANCED VEHICLE DETECTION IN LOW-LIGHT ON-BOARD ENVIRONMENTS

Abstract

Accurate vehicle detection in low-light environments remains a critical challenge in the field of intelligent transportation systems and autonomous driving. On-board vision-based detection systems often suffer from degraded performance due to insufficient illumination, motion blur, glare from headlights, and high levels of visual noise. To address these issues, this work presents a YOLOv10- driven enhanced vehicle detection framework tailored for real-time deployment in low-light on-board environments. The proposed system leverages the advanced feature extraction and optimized architecture of YOLOv10, which provides significant improvements in detection accuracy, computational efficiency, and robustness compared to its predecessors. In the framework, pre-processing techniques such as adaptive histogram equalization, noise reduction, and contrast enhancement are integrated to improve image quality before feeding data into the detection pipeline. YOLOv10 is then fine-tuned with a diverse dataset of nighttime and low-illumination driving scenarios to enhance generalization under challenging conditions. The lightweight yet powerful design of YOLOv10 enables real-time inference on embedded systems, making it suitable for invehicle applications where processing speed and resource efficiency are crucial. Experimental evaluations demonstrate that the YOLOv10-based model achieves superior performance in terms of mean Average Precision (mAP), detection speed, and false-positive reduction when compared to baseline models such as YOLOv8 and Faster R-CNN. Additionally, the system exhibits resilience to common low-light issues such as shadow occlusion, glare from artificial lighting, and environmental noise, ensuring reliable detection across diverse nighttime driving conditions.