Using Layer-Wise Training for Road Semantic Segmentation in Autonomous Cars

A recently developed application of computer vision is pathfinding in self-driving cars. Semantic scene understanding and semantic segmentation, as subfields of computer vision, are widely used in autonomous driving. Semantic segmentation for pathfinding uses deep learning methods and various large sample datasets to train a proper model. Due to the importance of this task, accurate and robust models should be trained to perform properly in different lighting and weather conditions and in the presence of noisy input data. In this paper, we propose a novel learning method for semantic segmentation called layer-wise training and evaluate it on a light efficient structure called an efficient neural network (ENet). The results of the proposed learning method are compared with the classic learning approaches, including mIoU performance, network robustness to noise, and the possibility of reducing the size of the structure on two RGB image datasets on the road (CamVid) and off-road (Freiburg Forest) paths. Using this method partially eliminates the need for Transfer Learning. It also improves network performance when input is noisy.

Automated summary

A recently developed application of computer vision is pathfinding in self-driving cars, which involves semantic scene understanding and semantic segmentation. Deep learning methods and large sample datasets are used for training accurate and robust models for pathfinding. The proposed learning method, called layer-wise training, is evaluated on the efficient neural network (ENet) structure, and compared with classic learning approaches on two RGB image datasets for road and off-road paths.