An ANN-based Real-time Unstructured Road Detection Approach under Time-varying Illumination

Road detection is a key problem in the application of autonomous driving and navigation. However, most of the recent approaches only obtain reliable results in certain well-arranged scenes. In this paper, we propose an online-learning road detection method for robust and real-time unstructured road detection in challenging scenes. Firstly, we describe an improved adaptive gamma correction method compensate for nonuniform illumination under rapidly changing illuminate conditions. Then we divide the image with a grid into suitable sized rectangular regions (cells), and some few cells are labeled as train/test samples. Finally, we adopt the online learning process through artificial neural networks so that our method can be adaptive to the varied environment. Experiments on the challenging database demonstrate the real-time capability, adaptability and reliability of our approach to varied illumination and complex scenarios.