Images acquired in low-light environments always suffer from low brightness， color distortion， loss of detail information， low contrast， and other problems. To meet the needs of subjective visual experience， researchers often enhance the images. However， the impact of image enhancement on the performance of machine vision applications is not systematically researched. In this paper， we first summarize typical low-light image enhancement methods and semantic segmentation methods. Next， we take a machine vision application （i.e.， semantic segmentation） as an example and select the low-light scene to investigate the effect of image enhancement methods on the semantic segmentation performance of the low-light scene. The experimental results show that enhancement processing can improve the visual effect of images， but may introduce noise. In addition， image enhancement methods and semantic segmentation methods do not concentrate exactly on the same focus and features. Therefore， image enhancement doesnot contribute significantly to the performance of semantic segmentation in low-light scenes， and even brings negative effects.