# Adaptive Low-Light Enhancement for Road Anomaly Detection: A Multi-Architecture Benchmark and Edge Deployment Study

**Authors:** Obed Honour Eje, Oluwatosin Kolade, Glory Bagai, Taliha Abiodun Folorunso  
**Lab:** Federal University of Technology Minna · **Published:** 5 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.08393) · [PDF](https://arxiv.org/pdf/2611.08393) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.08393.html)

## Abstract

Road anomaly detection systems on autonomous vehicles must function reliably under degraded visual conditions including darkness, rain, and Harmattan dust haze. This paper benchmarks four object detection architectures, YOLOv8n, YOLOv12n, RT-DETR, and Faster R-CNN, under physically grounded lighting and weather degradation, and shows that uniform application of the SCI low-light enhancement network is actively harmful at dusk-level lighting (-16.1% to -30.7% mAP@50). We propose a lightweight, training-free luminance-adaptive router that selects between no enhancement, moderate SCI, and denoising plus strong SCI based on per-frame Rec. 601 luminance, recovering most of the dusk penalty while preserving low-light gains, and we validate the full pipeline on NVIDIA Jetson Nano edge hardware.

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