YOLO-Night: Lighting the Path for Autonomous Vehicles with Robust Nighttime Perception.

Despite substantial progress in visual perception, object detection systems for autonomous driving still exhibit pronounced performance degradation in nighttime and low-light conditions, where reduced signal-to-noise ratio, blurred object boundaries, and scale ambiguity challenge reliable recognition. Existing YOLO-based detectors, primarily optimized for daytime imagery, struggle to maintain robustness under such adverse illumination. To address these issues, we propose YOLO-Night, a nighttime-oriented object detection framework that enhances the YOLO11 architecture through a structured integration of contrast enhancement, adaptive receptive field modeling, and multi-scale feature fusion. The framework incorporates a feature-level enhancement mechanism to improve low-contrast representations, employs depthwise switchable atrous convolution to dynamically adapt receptive fields for blurred and small objects, and introduces a multi-scale convolutional block to strengthen feature extraction under severe illumination degradation. In addition, a staged feature fusion strategy with an auxiliary low-level detection head was adopted to mitigate semantic misalignment across feature scales. Extensive experiments on the NightCity dataset demonstrated that YOLO-Night consistently outperformed the YOLO11n baseline, achieving improvements of +14.3% precision, +12.4% recall, and +10.4% mAP@50 under nighttime conditions while maintaining real-time inference capability. These results indicate that targeted architectural adaptations can substantially improve object detection robustness in low-light driving scenarios.