A novel simple light-weight Neural Network for Road Segmentation

Abstract

This study proposes simple methods to design a light-weight neural network. A deep learning domain has many state of the art neural networks so it is highly accurate for commonly used dataset such as ImageNet and Cifar-10, Cifar-100 and allows a rapid execution time and a small model. However, these state of the art neural networks are very complicated. This paper uses a VGG-16[1] model to reduce the size of the model and the inference time, but maintain accuracy. The semantic segmentation performance for the proposed method is compared to that for the VGG-16 model. The same full convolutional network (FCN) semantic segmentation algorithm [2] is used to compare the two models for the same semantic segmentation task. This study proposes an easier method to construct a light-weight neural network.