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YOLO-Night: Lighting the Path for Autonomous Vehicles with Robust Nighttime Perception
Jinxin Tian1, Muhammad Arslan Ghaffar2,3, Zhaokai Li4
1Henan Mechanical and Electrical Vocational College, Zhengzhou 451191, China.
Sensors (Basel, Switzerland)
|February 27, 2026
Summary
YOLO-Night enhances object detection for autonomous driving in low light. This new framework significantly improves precision and recall for nighttime driving scenarios.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Autonomous Systems
Background:
- Object detection systems struggle in low-light conditions due to noise and poor visibility.
- Existing YOLO models are optimized for daytime and perform poorly at night.
Purpose of the Study:
- To develop an object detection framework, YOLO-Night, specifically for nighttime autonomous driving.
- To enhance the YOLO11 architecture for improved low-light performance.
Main Methods:
- Integrated contrast enhancement and adaptive receptive field modeling.
- Employed depthwise switchable atrous convolution for dynamic receptive fields.
- Utilized multi-scale feature fusion with an auxiliary low-level detection head.
Main Results:
- YOLO-Night outperformed the YOLO11n baseline on the NightCity dataset.
- +14.3% precision, +12.4% recall, and +10.4% mAP@50 improvements under nighttime conditions.
- Maintained real-time inference capabilities.
Conclusions:
- Targeted architectural adaptations significantly boost object detection robustness in low-light driving.
- YOLO-Night offers a viable solution for reliable nighttime autonomous driving perception.
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