LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning

Md Al-Masrur Khan; Md Foysal Haque; Kazi Rakib Hasan; Samah H Alajmani; Mohammed Baz; Mehedi Masud; Abdullah-Al Nahid

doi:10.3390/s22155595

LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning

Sensors (Basel). 2022 Jul 26;22(15):5595. doi: 10.3390/s22155595.

Authors

Md Al-Masrur Khan¹, Md Foysal Haque², Kazi Rakib Hasan³, Samah H Alajmani⁴, Mohammed Baz⁵, Mehedi Masud⁶, Abdullah-Al Nahid³

Affiliations

¹ Department of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, Busan 49315, Korea.
² Department of Electronic Engineering, Dong-A University, Busan 49315, Korea.
³ Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.
⁴ Department of Information Technology, College of Computer and Information Technology, Taif University, Taif 21994, Saudi Arabia.
⁵ Department of Computer Engineering, College of Computer and Information Technology, Taif University, Taif 21994, Saudi Arabia.
⁶ Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia.

Abstract

Lane detection plays a vital role in making the idea of the autonomous car a reality. Traditional lane detection methods need extensive hand-crafted features and post-processing techniques, which make the models specific feature-oriented, and susceptible to instability for the variations on road scenes. In recent years, Deep Learning (DL) models, especially Convolutional Neural Network (CNN) models have been proposed and utilized to perform pixel-level lane segmentation. However, most of the methods focus on achieving high accuracy while considering structured roads and good weather conditions and do not put emphasis on testing their models on defected roads, especially ones with blurry lane lines, no lane lines, and cracked pavements, which are predominant in the real world. Moreover, many of these CNN-based models have complex structures and require high-end systems to operate, which makes them quite unsuitable for being implemented in embedded devices. Considering these shortcomings, in this paper, we have introduced a novel CNN model named LLDNet based on an encoder-decoder architecture that is lightweight and has been tested in adverse weather as well as road conditions. A channel attention and spatial attention module are integrated into the designed architecture to refine the feature maps for achieving outstanding results with a lower number of parameters. We have used a hybrid dataset to train our model, which was created by combining two separate datasets, and have compared the model with a few state-of-the-art encoder-decoder architectures. Numerical results on the utilized dataset show that our model surpasses the compared methods in terms of dice coefficient, IoU, and the size of the models. Moreover, we carried out extensive experiments on the videos of different roads in Bangladesh. The visualization results exhibit that our model can detect the lanes accurately in both structured and defected roads and adverse weather conditions. Experimental results elicit that our designed method is capable of detecting lanes accurately and is ready for practical implementation.

Keywords: autonomous cars; convolutional neural network; deep learning; lane detection; semantic segmentation.

MeSH terms

Automobiles*
Deep Learning*
Image Processing, Computer-Assisted / methods
Neural Networks, Computer
Spectrum Analysis, Raman
Weather

Grants and funding

TURSP-2020/239/This work was supported by Taif University Researchers Supporting Project number (TURSP-2020/239), Taif University, Taif, Saudi Arabia.