Towards lane detection using a generative adversarial network




segmentation, machine learning, neural networks, color spaces, TuSimple, GAN, traffic accidents, assitance systems, highways, collisions


Traffic accidents are one of the main causes of death in Mexico, the collisions are caused mostly due to human error, therefore attempts have been made to reduce these shortcomings with driver assistance systems. This paper presents a study conducted to explore the capabilities of a Generative Adversarial Network in terms of application in lane detection on a highway, it is proposed to use a metric known as Dice index which measures the similarity between images and a pre-processing method based on color spaces, as well as a technique called Superpixels which is based on clustering. Finally, the results are compared with a neural network called LaneNet developed for the TuSimple database. The results obtained from this methodology needs to be optimized with future work, however, it opens the door to possible research with this type of network.


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Author Biographies

Tomás Emmanuel Juárez Vallejo , Autonomous University of Queretaro

Facultad de Ingeniería. Querétaro, México

Sebastián Salazar Colores, Optical Research Center

León, Guanajuato, México

Juan Manuel Ramos Arreguín, Autonomous University of Queretaro

Faculty of Engineering. Queretaro, Mexico


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How to Cite

Juárez Vallejo , T. E., Salazar Colores, S., & Ramos Arreguín, J. M. (2023). Towards lane detection using a generative adversarial network. Nova Scientia, 15(31), 1–11.



Natural Sciences and Engineering


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