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

Noor Jannah Zakaria; Mohd Ibrahim Shapiai; Rasli Abd Ghani; Nurbaiti Wahid; Daphne Teck Ching Lai

doi:10.37934/araset.39.2.166180

Authors

Noor Jannah Zakaria Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia
Mohd Ibrahim Shapiai Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia
Rasli Abd Ghani Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia
Nurbaiti Wahid College of Engineering, Universiti Teknologi Mara, Dungun, Terengganu, Malaysia
Daphne Teck Ching Lai School of Digital Science, Universiti Brunei Darussalam, Negara Brunei Darussalam

DOI:

https://doi.org/10.37934/araset.39.2.166180

Keywords:

Fully convolutional network, attention mechanism, Kitti lane dataset, deep learning model, autonomous vehicle

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.