Image Enhancement Algorithms to Improve Robustness and Accuracy of Pre-trained Neural Networks for Autonomous Driving

Abstract

This research proposes a generalized data-driven approach for improving the pre-training image datasets fed to neural networks(NNs). The algorithms developed and tested in this work could substantially enhance the image bringing out the critical spatial information in the image for better NN performance. This image enhancement technique consists of two main components: image colorization and contrast enhancement. Image colorization is implemented to obtain a color-corrected image from a grayscale image. The traditional global contrast enhancement algorithm is extended to Smoothened Variable Local Dynamic Contrast Improvement (SVL-DCI) to boost local contrasts within an image frame that suffers from under/over-exposed lighting conditions. SVL-DCI algorithm is developed and thoroughly tested in the present thesis that could run in real-time as a pre-training algorithm for NNs. We implemented SVL-DCI on the 3P lab dataset of 2470 images and observed competitive improvement in the performance of the investigated NNs for object recognition and lane detection.