Abstract
Autonomous vehicles aim at higher levels of intelligence to recognize all the elements in the surrounding environment; in order to be able to make decisions efficiently and in real time. For this reason, a convolutional neural networks capable of perform semantic segmentation of these elements have been implemented. In this work it is proposed to use the ERFNet architecture to segment the main obstacles and lanes in a road environment. One of the requirements for training this type of networks is to have a complete and large dataset with these two types of labels. In order to avoid manual labeling, an automatic way of carrying out this process is proposed, using convolutional neural networks and different dataset already labeled. The generated dataset contains 19000 images tagged with obstacles and lanes, to be used to train a network of ERFnet architecture. From the experiment, the obtained results show the performance of the proposed approach providing accuracy of 74.42%.
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Notes
- 1.
This dataset is published in Github: https://github.com/lsi-uc3m/lsiold.
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Acknowledgment
Research supported by the Spanish Government through the CICYT projects (TRA2016-78886-C3-1-R and RTI2018-096036-B-C21), and the Comunidad de Madrid through SEGVAUTO-4.0-CM (P2018/EMT-4362). Also, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research.
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Cabrera Lo Bianco, L., Al-Kaff, A., Beltrán, J., García Fernández, F., Fernández López, G. (2020). Joint Instance Segmentation of Obstacles and Lanes Using Convolutional Neural Networks. In: Silva, M., Luís Lima, J., Reis, L., Sanfeliu, A., Tardioli, D. (eds) Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in Intelligent Systems and Computing, vol 1092. Springer, Cham. https://doi.org/10.1007/978-3-030-35990-4_19
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