Robust Low-Light Pedestrian Detection via Optimized Zero-DCE and YOLOv8

Abstract

Visual sensors are fundamental components in intelligent surveillance and autonomous transportation systems. However, in low-illumination scenarios, the reliability of these sensors is severely compromised by physical limitations, such as signal attenuation, high noise levels, and reduced dynamic range, which degrade the performance of downstream perception tasks. To address these challenges, this paper proposes a robust visual sensing framework that integrates adaptive signal enhancement with efficient object detection. object detection. First, to mitigate sensor data degradation, we introduce an improved Zero-Reference Deep Curve Estimation (Zero-DCE) algorithm. This module incorporates anisotropic diffusion for noise suppression while preserving structural edges and integrates a non-local attention mechanism to capture long-range dependencies in the sensor inputs. A dynamic exposure loss function is further employed to adaptively adjust brightness. Second, to ensure efficient interpretation of the enhanced data, we optimize the YOLOv8 architecture by introducing a lightweight C2-star block and the MPDIoU loss function, balancing detection accuracy with the efficient requirements of edge sensing devices. Experimental results on benchmark datasets demonstrate that the proposed framework significantly improves signal quality (PSNR of 17.44 dB on LIME) and detection precision (improving mAP by 1.8%). This work provides an effective solution for robust pedestrian monitoring in adverse lighting conditions, enhancing the environmental perception capabilities of intelligent sensing systems.