Revolutionizing navigation: Deep Learning for lane detection in mobile robots

Mohammed Hammoud, Ola Haydar, Ammar Asad, Zeinab Khadra, Sergey Sergeevich Lupin

Abstract


Mobile robots are becoming increasingly vital across various industries because of their ability to perform tasks safely, efficiently, and autonomously. A key feature of these robots and self-driving cars is lane detection, which can be particularly challenging owing to changing lighting and weather conditions. This study focuses on detecting lanes using deep learning-based image segmentation techniques. It employs transfer learning from pre-trained models on the ImageNet dataset, specifically examining architectures such as LinkNet, Unet, and Unet++. The study also explored various model backbones, including ResNet, VGG, Inception, and MobileNet. A benchmark dataset was utilized alongside data augmentation techniques to enhance the training volume and ensure robust validation. The results indicated that U-Net demonstrated the highest performance among all tested models, achieving an Intersection over Union (IoU) score of 0.848. This study contributes significantly to the development of reliable lane detection systems for mobile robotic applications.

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References


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