Day and Night Place Recognition Based on Low-quality Night-time Images

Place recognition refers to the problem of finding the position of a query image based on a series of images acquired at different places. Yet the day and night place recognition problem is hard to solve due to the illumination and appearance changes. Image-to-image translation methods have been introduced to solve the place recognition problem by synthesizing daytime images from the night ones. However, these methods cannot achieve good translation performance with low-quality night-time images. In this paper, a new method is introduced to improve the quality of night-time restored images by combining image enhancement and image inpainting methods. Three kinds of enhanced night-time images are generated based on the proposed method. Our place recognition system includes a model of GoogleNet to generate deep features of input images and nearest neighbor searching for the image retrieval process. The approach is tested on the Oxford RobotCar dataset, where three low-quality night sequences are selected as query sequences, and a day sequence is selected as a reference sequence. The results obtained with the approach based on the three proposed enhanced night-time images are better than those obtained with the raw night-time images. The results of our proposed place recognition system are also compared with two state-of-art place recognition methods: ToDayGAN and densevlad.