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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Immanuel Johnraja Jebadurai
Karunya Institute of Technology and Sciences

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FedLANE: a federated U-Net architecture for lane detection Santhiya Santhiya; Immanuel Johnraja Jebadurai; Getzi Jeba Leelipushpam Paulraj; Polisetti Pavan Venkata Vamsi; Madireddy Aravind Reddy; Praveen Poulraju
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1621-1629

Abstract

Lane detection is a crucial module for today’s autonomous driving cars. Detecting road lanes is a challenging task as it varies in color, texture, boundaries and markings. Traditional lane detection techniques detect the lane by applying a model trained with centralized data. As roads vary in urban and rural areas, a more localized and decentralized training technique is desired for accurate and personalized lane detection. Federated learning has recently proved to be a promising technology that trains and prunes the model using local data. Applying federated learning-based lane detection improves the accuracy of detection and also ensures the security and privacy of autonomous cars. This paper proposes FedLANE, a federated learning-based lane detection technique. U-Net, U-Net long short-term memory (LSTM) and AU-Net architectures were explored using a federated learning approach. Experimental analysis using TuSimple and CuLane dataset shows that the FedLANE based lane detection performs similar to that of the traditional deep learning lane detection models.
Co-Authors Getzi Jeba Leelipushpam Paulraj Madireddy Aravind Reddy Polisetti Pavan Venkata Vamsi Praveen Poulraju Santhiya Santhiya