  In recent years, image and video surveillance have made considerable
progresses to the Intelligent Transportation Systems (ITS) with the help of
deep Convolutional Neural Networks (CNNs). As one of the state-of-the-art
perception approaches, detecting the interested objects in each frame of video
surveillance is widely desired by ITS. Currently, object detection shows
remarkable efficiency and reliability in standard scenarios such as daytime
scenes with favorable illumination conditions. However, in face of adverse
conditions such as the nighttime, object detection loses its accuracy
significantly. One of the main causes of the problem is the lack of sufficient
annotated detection datasets of nighttime scenes. In this paper, we propose a
framework to alleviate the accuracy decline when object detection is taken to
adverse conditions by using image translation method. We propose to utilize
style translation based StyleMix method to acquire pairs of day time image and
nighttime image as training data for following nighttime to daytime image
translation. To alleviate the detail corruptions caused by Generative
Adversarial Networks (GANs), we propose to utilize Kernel Prediction Network
(KPN) based method to refine the nighttime to daytime image translation. The
KPN network is trained with object detection task together to adapt the trained
daytime model to nighttime vehicle detection directly. Experiments on vehicle
detection verified the accuracy and effectiveness of the proposed approach.
