Fully Convolutional Network Model Applied Attention Mechanism on Kitti Lane Dataset for Lane Detection

Abstract

This article investigated the attention mechanism implemented by the Fully Convolutional Network (FCN) Model on the Kitti Lane Dataset. Two attention mechanisms were applied in the deep learning model to improve traffic lane detection for autonomous vehicles. The Kitti lane dataset, which was generated in collaboration with Jannik Fritsch and Tobias Kuehl from Honda Research Europe GmbH, was selected for this study. The results demonstrate that the applied attention mechanism can effectively improve the network's feature representation on lane markings. Furthermore, this approach can improve the weighted information of lane line targets while decreasing irrelevant information. As a result, the proposed technique improved, obtaining more than 95% accuracy. Subsequently, the attention mechanism was implemented in the FCN model architecture to enhance the lane-detecting model. As a result, in the future, more comprehensive ideas, such as combining the FCN model with Transfer Learning, will play an essential part in investigating the improvement of lane detection areas.