Dark-DENet: A Lightweight Enhancement Network for Low-Light Object Detection

Abstract

Deep learning-based object detection methods have shown significant success, particularly in robotic vision tasks like autonomous navigation and object manipulation. However, their performance drops sharply in low-light conditions, challenging robots in poorly lit environments. To address this, we propose Dark-DENet, a lightweight detection-driven enhancement network specifically designed for low-light conditions. Dark-DENet introduces an Improved Global Enhancement Module for low-frequency components to capture multiscale features, and a multi-layer convolutional structure in the Detail Enhancement Module to enhance high-frequency components. Additionally, the Scale-Aware Pooling Fusion Module enriches the semantic information of HF components. Dark-DENet is a plug-and-play network that can be easily integrated into the backbone of various detectors for joint training. Integrated with YOLOv5 as DD-YOLO, and combined with other models like YOLO series, RT-DETR, RetinaNet, and Faster R-CNN, experimental results show Dark-DENet consistently improves detection performance across all models. It effectively enhances latent features under limited runtime, making it a robust solution for robotic vision in low-light environments.