KLASIFIKACIJA TIPA RADARSKOG OBJEKTA KORISTEĆI LSTM NEURONSKE MREŽE SA KONVOLUCIJAMA NA SKUPOVIMA TAČAKA
Ključne reči:
Veštačka inteligencija, neuronske mreže, radarska klasifikacija
Apstrakt
Rad predstavlja model neuronskih mreža za klasifikaciju tipa radaskog objekta na osnovu skupa tačaka koje definišu objekat. Arhitektura koristi konvolucije na neuređenim skupovima tačaka u okviru kratkotrajno-dugotrajne memorijske ćelije da enkodira geometriju i osobine tačaka kroz vreme. Model pokazuje visoke mere performansi na skupu podataka View of Delft sa 94% tačnosti i 0.94 F1 merom na klasama pešaka, automobila i biciklista.
Reference
[1] Qi, C.R., Su, H., Mo, K. and Guibas, L.J., 2017. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 652-660).
[2] Qi, C.R., Yi, L., Su, H. and Guibas, L.J., 2017. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems, 30.
[3] Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F. and Guibas, L.J., 2019. Kpconv: Flexible and deformable convolution for point clouds. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 6411-6420).
[4] Palffy, A., Pool, E., Baratam, S., Kooij, J.F. and Gavrila, D.M., 2022. Multi-class road user detection with 3+ 1D radar in the View-of-Delft dataset. IEEE Robotics and Automation Letters, 7(2), pp.4961-4968.
[5] Nobis, F., Fent, F., Betz, J. and Lienkamp, M., 2021. Kernel point convolution LSTM networks for radar point cloud segmentation. Applied Sciences, 11(6), p.2599.
[6] Fent, F., 2020. Machine Learning-Based Radar Point Cloud Segmentation.
[7] Fan, H. and Yang, Y., 2019. PointRNN: Point recurrent neural network for moving point cloud processing. arXiv preprint arXiv:1910.08287.
[8] Schumann, O., Hahn, M., Dickmann, J. and Wöhler, C., 2018, July. Semantic segmentation on radar point clouds. In 2018 21st International Conference on Information Fusion (FUSION) (pp. 2179-2186). IEEE.
[2] Qi, C.R., Yi, L., Su, H. and Guibas, L.J., 2017. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems, 30.
[3] Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F. and Guibas, L.J., 2019. Kpconv: Flexible and deformable convolution for point clouds. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 6411-6420).
[4] Palffy, A., Pool, E., Baratam, S., Kooij, J.F. and Gavrila, D.M., 2022. Multi-class road user detection with 3+ 1D radar in the View-of-Delft dataset. IEEE Robotics and Automation Letters, 7(2), pp.4961-4968.
[5] Nobis, F., Fent, F., Betz, J. and Lienkamp, M., 2021. Kernel point convolution LSTM networks for radar point cloud segmentation. Applied Sciences, 11(6), p.2599.
[6] Fent, F., 2020. Machine Learning-Based Radar Point Cloud Segmentation.
[7] Fan, H. and Yang, Y., 2019. PointRNN: Point recurrent neural network for moving point cloud processing. arXiv preprint arXiv:1910.08287.
[8] Schumann, O., Hahn, M., Dickmann, J. and Wöhler, C., 2018, July. Semantic segmentation on radar point clouds. In 2018 21st International Conference on Information Fusion (FUSION) (pp. 2179-2186). IEEE.
Objavljeno
2024-11-05
Sekcija
Elektrotehničko i računarsko inženjerstvo
