End-to-end adaptive object detection with learnable Retinex for low-light city environment

Abstract

ABSTRACT In the smart city context, efficient urban surveillance under low-light conditions is crucial. Accurate object detection in dimly lit areas is vital for safety and nighttime driving. However, subpar, poorly lit images due to environmental or equipment limitations pose a challenge, affecting precision in tasks like object detection and segmentation. Existing solutions often involve time-consuming, inefficient image preprocessing and lack strong theoretical support for low-light city image enhancement. To address these issue, we propose an end-to-end pipeline named LAR-YOLO that leverages convolutional network to extract a set of image transformation parameters, and implements the Retinex theory to proficiently elevate the quality of the image. Unlike conventional approaches, this innovative method eliminates the need for hand-crafted parameters and can adaptively enhance each low-light image. Additionally, due to a restricted quantity of training data, the detection model may not achieve an adequate level of expertise to enhance detection accuracy. To tackle this challenge, we introduce a cross-domain learning approach that supplements the low-light model with knowledge from normal light scenarios. Our proof-of-principle experiments and ablation studies utilising ExDark and VOC datasets demonstrate that our proposed method outperforms similar low-light object detection algorithms by approximately 13% in terms of accuracy.