Análisis de algoritmos de detección de objetos para la creación de un prototipo basado en la fusión de dos modelos de reconocimiento
DOI:
https://doi.org/10.29018/issn.2588-1000vol3iss20.2019pp5-10Palabras clave:
RPN. FRUSTUN, KITTI, LIDAR, CNNResumen
En el presente estudio se muestra una metodología experimental deductiva basada en redes neuronales para el reconocimiento de objetos con el uso de CNN. Nuestro objetivo es generar un prototipo el cual está basado en un mapa de características en combinación con RPN y propuesta de recorte en tronco que usa TNET para la detección 3D, dado por modelos de reconocimiento de objetos de la plataforma KITTI ,enfocados especialmente en AVOD y FPOINTNET, obteniendo una mayor precisión en objetos más pequeños que fácilmente son descartables por la nube de puntos proporcionados por el sensor laser 3d LIDAR HDL-64 pero no por el mapeado de características.
Descargas
Los datos de descargas todavía no están disponibles.
Citas
Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882
Zachary C. Lipton, John Berkowitz, (2015), A Critical Review of Recurrent Neural Networks for Sequence Learning, Machine Learning
Li, X., Hong, C., Yang, Y. & Wu, X. (2013). Deep neural networks for syllable based acoustic modeling in Chinese speech recognition.. APSIPA (p./pp. 1-4), : IEEE. ISBN: 978-1-4799-2794-4
Iandola, F. N., Moskewicz, M. W., Ashraf, K., Han, S., Dally, W. J. & Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size.. CoRR, abs/1602.07360
Yin Zhou, Oncel Tuzel, VoxelNet, (2017), End-to-End Learning for Point Cloud Based 3D Object Detection,IEEE Conference on Computer Vision and Pattern Recognition
Song, S., Lichtenberg, S. P. & Xiao, J. (2015). SUN RGB-D: A RGB-D scene understanding benchmark suite.. CVPR (p./pp. 567-576), IEEE Computer Society. ISBN: 978-1-4673-6964-0
A. Geiger, P. Lenz, and R. Urtasun, (2012), Are we ready for autonomous driving? the kitti vision benchmark suite,IEEE Conference on Computer Vision and Pattern Recognition
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, (2017), Multi-view 3d object detection network for autonomous driving. IEEE Conference on Computer Vision and Pattern Recognition
Ku, J., Mozifian, M., Lee, J., Harakeh, A. & Waslander, S. L. (2017). Joint 3D Proposal Generation and Object Detection from View Aggregation.. CoRR, abs/1712.02294
Charles R. Qi,Wei Liu, Chenxia Wu, Hao Su, Leonidas J. Guibas , Stanford University, Nuro Inc. ,UC San Diego, 13 Apr 2018 , Frustum PointNets for 3D Object Detection from RGB-D Data,IEEE Conference on Computer Vision and Pattern Recognition
Li, X., Bing, L., Lam, W. & Shi, B. (2018). Transformation Networks for Target-Oriented Sentiment Classification.. In I. Gurevych & Y. Miyao (eds.), ACL (1) (p./pp. 946-956), Association for Computational Linguistics. ISBN: 978-1-948087-32-2
C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi, and A. C. Berg, (2017),Deconvolutional single shot detector,IEEE Conference on Computer Vision and Pattern Recognition
Redmon, J., Divvala, S., Girshick, R., Farhadi, A, (2016) You only look once: Unified, real-time object detection,IEEE Conference on Computer Vision and Pattern Recognition,1,(1-5)
Tsung-Yi Lin, Piotr Dollar, Ross Girshick,Kaiming He, Bharath Hariharan, and Serge Belongie, (2017), Feature Pyramid Networks for Object Detection, Computer Vision and Pattern Recognition, 1-2, (1-2-3-4-5-6-7)
Simonyan, K. & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR, abs/1409.1556
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L, (2015), Imagenet large scale visual recognition challenge, International Journal of Computer Vision, 1, (1-2-3-5-6)
Li, B. (2016). 3D Fully Convolutional Network for Vehicle Detection in Point Cloud.. CoRR, abs/1611.08069. (1,2)
Qi, C. R., Su, H., Mo, K. & Guibas, L. J. (2017). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation.. CVPR (p./pp. 77-85), (2,5,6,8)
Zachary C. Lipton, John Berkowitz, (2015), A Critical Review of Recurrent Neural Networks for Sequence Learning, Machine Learning
Li, X., Hong, C., Yang, Y. & Wu, X. (2013). Deep neural networks for syllable based acoustic modeling in Chinese speech recognition.. APSIPA (p./pp. 1-4), : IEEE. ISBN: 978-1-4799-2794-4
Iandola, F. N., Moskewicz, M. W., Ashraf, K., Han, S., Dally, W. J. & Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size.. CoRR, abs/1602.07360
Yin Zhou, Oncel Tuzel, VoxelNet, (2017), End-to-End Learning for Point Cloud Based 3D Object Detection,IEEE Conference on Computer Vision and Pattern Recognition
Song, S., Lichtenberg, S. P. & Xiao, J. (2015). SUN RGB-D: A RGB-D scene understanding benchmark suite.. CVPR (p./pp. 567-576), IEEE Computer Society. ISBN: 978-1-4673-6964-0
A. Geiger, P. Lenz, and R. Urtasun, (2012), Are we ready for autonomous driving? the kitti vision benchmark suite,IEEE Conference on Computer Vision and Pattern Recognition
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, (2017), Multi-view 3d object detection network for autonomous driving. IEEE Conference on Computer Vision and Pattern Recognition
Ku, J., Mozifian, M., Lee, J., Harakeh, A. & Waslander, S. L. (2017). Joint 3D Proposal Generation and Object Detection from View Aggregation.. CoRR, abs/1712.02294
Charles R. Qi,Wei Liu, Chenxia Wu, Hao Su, Leonidas J. Guibas , Stanford University, Nuro Inc. ,UC San Diego, 13 Apr 2018 , Frustum PointNets for 3D Object Detection from RGB-D Data,IEEE Conference on Computer Vision and Pattern Recognition
Li, X., Bing, L., Lam, W. & Shi, B. (2018). Transformation Networks for Target-Oriented Sentiment Classification.. In I. Gurevych & Y. Miyao (eds.), ACL (1) (p./pp. 946-956), Association for Computational Linguistics. ISBN: 978-1-948087-32-2
C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi, and A. C. Berg, (2017),Deconvolutional single shot detector,IEEE Conference on Computer Vision and Pattern Recognition
Redmon, J., Divvala, S., Girshick, R., Farhadi, A, (2016) You only look once: Unified, real-time object detection,IEEE Conference on Computer Vision and Pattern Recognition,1,(1-5)
Tsung-Yi Lin, Piotr Dollar, Ross Girshick,Kaiming He, Bharath Hariharan, and Serge Belongie, (2017), Feature Pyramid Networks for Object Detection, Computer Vision and Pattern Recognition, 1-2, (1-2-3-4-5-6-7)
Simonyan, K. & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR, abs/1409.1556
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L, (2015), Imagenet large scale visual recognition challenge, International Journal of Computer Vision, 1, (1-2-3-5-6)
Li, B. (2016). 3D Fully Convolutional Network for Vehicle Detection in Point Cloud.. CoRR, abs/1611.08069. (1,2)
Qi, C. R., Su, H., Mo, K. & Guibas, L. J. (2017). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation.. CVPR (p./pp. 77-85), (2,5,6,8)
Descargas
Publicado
2019-03-29
Cómo citar
Quiroz Martínez, M., Pangay Zambrano, C. P., & Pérez Macías, K. J. (2019). Análisis de algoritmos de detección de objetos para la creación de un prototipo basado en la fusión de dos modelos de reconocimiento. Pro Sciences: Revista De Producción, Ciencias E Investigación, 3(20), 5–10. https://doi.org/10.29018/issn.2588-1000vol3iss20.2019pp5-10
Número
Sección
ARTÍCULO DE INVESTIGACIÓN
