Accelerating Colorizer of Shaded Image for Autonomous Driving in Resource-Constrained SoC

Donghee Ha, Jinse Kwon, Hyungshin Kim

Abstract: Image-based convolutional neural network(CNN) algorithms are spreading across a variety of applications. In particular, an autonomous vehicle recognizes objects and the surrounding situation using the CNN models. CNN models for image classification are trained using clear image dataset, so they are not robust to grayscale images or noise-intensive data. Therefore, there is a risk of an accident because the quality of the input image drops rapidly during night driving. The region that is revealed by the headlight can have colors, but in the shaded area it has a brightness that is not enough to get color values. We intend to increase the safety of autonomous driving by coloring this region of interest(ROI).