Style Transfer Deep Learning Framework for Nighttime Robust Vehicle Detection in On-Road Mobile Platforms

Kyeongseon Kim, Joongheon Kim

http://doi.org/10.5626/JOK.2019.46.9.968

Car recognition has become an important part of self-driving car technologies. In autonomous driving, vehicle detection techniques are important to prevent vehicle-to-vehicle accidents. Traditional image processing methods for vehicle detection perform car detection via deep learning. Studies indicate that although these methods are effective in more than fifty percent of cases in daytime detection, their performance is insufficient for nighttime recognition. Vehicle detection is one of the tasks involved in minimizing the loss of human lives. Further, the nighttime scenario is more common, and therefore, in this paper, we propose an improved and robust method for detection of the car via filter-based image style transfer. The results of the proposed method were obtained using real-world data and experiments, and indicate the superiority of our method compared with other methods in terms of accuracy of ideal segmentation.