Red Neuronal semántica para la detección de carriles peatonales

Juan Manuel  Aldana Porras; John Fredy  Montes Mora

doi:10.22490/25394088.7476

Vol. 17 No. 4 (2023): Edición especial Expotech

Published 2023-12-11

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Original article

Semantic Neural Network for pedestrian lane detection

DOI: https://doi.org/10.22490/25394088.7476

Juan Manuel Aldana Porras Universidad Nacional Abierta y a Distancia

John Fredy Montes Mora Universidad Nacional Abierta y a Distancia

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Abstract
References

This work is part of the PG2402ECBTI2022 project approved by the Universidad Nacional Abierta y a Distancia. Its objective is to develop a spatial perception system with artificial intelligence (AI) techniques to improve the orientation and mobility of visually impaired people in Ibagué. The proposed system consists of several modules, and this text focuses on the results of the image processing module oriented to the segmentation of pedestrian lanes through the use of semantic neural networks. The network proposed for this task is based on a Deeplab architecture and was trained on a dataset composed of 5443 images. The results show that the architecture obtained an accuracy level of 0.95 and that its capacity to generalize new samples is in accordance with the task required for the system in general.

keywords: Visually impaired, Neural semantic network, Deeplab

Ang, S. P., Phung, S. L., Bouzerdoum, A., Nguyen, T. N. A., Duong, S. T. M., & Schira, M. M. (2021). Real-time Pedestrian Lane Detection for Assistive Navigation using Neural Architecture Search. 2020 25th International Conference on Pattern Recognition (ICPR), 8392-8399. https://doi.org/10.1109/ICPR48806.2021.9412741

Antiga, L. P. G., Stevens, E., & Viehmann, T. (2020). Deep Learning with PyTorch. Simon and Schuster.

Bodine, C. (2013). Assistive Technology and Science. Thousand Oaks, Calif: SAGE Publications. Recuperado de http://search.ebscohost.com.bibliotecavirtual.unad.edu.co/login.aspx?direct=true&db=nlebk&AN=525937&lang=es&site=eds-live&scope=site

Bryant, D. P., & Bryant, B. R. (2003). Assistive technology for people with disabilities. Boston, MA: Pearson.

Buduma, N., Buduma, N., & Papa, J. (2022). Fundamentals of Deep Learning. O’Reilly Media, Inc.

Cabrera, F. (2019). Movilidad Urbana, espacio público y ciudadanos sin autonomía: El caso de Lima. (Tesis doctoral) Universidad Autónoma de Barcelona.https://ddd.uab.cat/pub/tesis/2019/hdl_10803_667392/icv1de1.pdf

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834-848. https://doi.org/10.1109/TPAMI.2017.2699184

Chollet, F. (2021). Deep Learning with Python, Second Edition. Simon and Schuster.

DANE. (2018). Censo Nacional de Población y Vivienda 2018. https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y-poblacion/censo-nacional-de-poblacion-y-vivenda-2018

Dey, S. (2018). Hands-On Image Processing with Python: Expert techniques for advanced image analysis and effective interpretation of image data. Packt Publishing Ltd.

Ekman, M. (2021). Learning deep learning: Theory and practice of neural networks, computer vision, nlp, and transformers using tensorflow (First edition). Addison-Wesley.

Gulli, A., Kapoor, A., & Pal, S. (2019). Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and More with TensorFlow 2 and the Keras API. Packt Publishing.

Kneusel, R. T. (2021). Math for Deep Learning: What You Need to Know to Understand Neural Networks. No Starch Press.

Patterson, J., & Gibson, A. (2017). Deep learning: A practitioner's approach. United States of America: " O'Reilly Media, Inc.".

Pérgolis, j. C., y moreno Hernández, D. (2009). La capacidad comunicante del espacio. Revista de Arquitectura, 11, 68-73.

Regalado, G. D. (2019). El capital de la movilidad urbana cotidiana: motilidad en la periferia de Lima Metropolitana. Revista de Arquitectura (Bogotá), 22(1). 67-81. https://doi.org/10.14718/RevArq.2020.3038

Smythe, K.., Adam, D. , da Silva, C. , & Okimoto, M. (2020). Including users in the evaluation process of assistive technology: Applied methods and techniques. Advances in Intelligent Systems and Computing, 972(1), 940–947. https://doi.org/10.1007/978-3-030-19135-1_92

Studer, S., Bui, T. B., Drescher, C., Hanuschkin, A., Winkler, L., Peters, S., & Müller, K. R. (2021). Towards CRISP-ML (Q): a machine learning process model with quality assurance methodology. Machine Learning and Knowledge Extraction, 3(2), 392-413.https://arxiv.org/pdf/2003.05155.pdf

Thanh Le, H., Phung, S. L., & Bouzerdoum, A. (2022). Bayesian Gabor Network With Uncertainty Estimation for Pedestrian Lane Detection in Assistive Navigation. IEEE Transactions on Circuits and Systems for Video Technology, 32(8), 5331-5345. https://doi.org/10.1109/TCSVT.2022.3144184

Vega, Maria. (2015). Ciudad,espacio y ceguera en ciudad Juárez México. Revista Latinoamericana de Estudios sobre Cuerpos, Emociones y Sociedad, 7(17),42-50.[fecha de Consulta 20 de Agosto de 2021]. ISSN: 1852-8759. Disponible en: https://www.redalyc.org/articulo.oa?id=273238564005

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How to Cite

Aldana Porras, J. M. ., & Montes Mora, J. F. . (2023). Semantic Neural Network for pedestrian lane detection. Publicaciones E Investigación, 17(4). https://doi.org/10.22490/25394088.7476

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