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

doi:10.1109/ACCESS.2023.3255988

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

2023-01-01

4

OA

AI

S

Shahrzad Shashaani

M

Mohammad Teshnehlab *

DOI：10.1109/ACCESS.2023.3255988

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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.

Keywords:

Autonomous vehicles

Semantic segmentation

Convolutional neural networks

Semantics

Feature extraction

Decoding

Computer vision

Autonomous cars

layer-wise trains

computer vision

convolution neural networks

semantic segmentation

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

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