N-LoLiGan: Unsupervised low-light enhancement GAN with an N-Net for low-light tunnel images

Abstract

Because of low luminance in tunnels, target features in images are not salient, which makes the detection of tunnel surface defects challenging. Most deep-learning-based low-light enhancement methods suffer from contrast distortion and insufficient brightness in extremely dark scenes, as well as artifacts and overexposure in tunnel scenes with large light and dark differences. To alleviate these problems, we propose a novel unsupervised generative adversarial network (GAN), called the N-shape low-light enhancement GAN (N-LoLiGan), which can be trained using unpaired low-/normal-light images and is proven to generalize sufficiently on tunnel images with complex lighting. Experimental results demonstrate that the proposed N-LoLiGan outperforms several state-of-the-art low-light image enhancement methods in terms of visual quality and two no-reference image quality metrics. Additionally, when the proposed method is used as a preprocessing step for the tunnel multi-target detection task, it yields a higher detection rate (88.9% and 97.23%) than other methods.