Abstract
With the recent availability of automotive data such as the Nuscenes dataset, sensor fusion for object detection has gained popularity in the field of autonomous driving. One of the most common sensor combinations is camera and radar. While vision has been studied for many years, the recent availability of radar data has added novelty to the field of object detection. This study explores the concept of radar data augmentation using methods inspired by commonly used image augmentation techniques. The model was trained on the Nuscenes mini dataset and a mean average precision (mAP) of 0.45 was achieved, 20.98% greater than the baseline results of the network.
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References
Geisslinger M (2019) Autonomous driving: “object detection using neural networks for radar and camera sensor fusion. Master’s thesis, Technical University of Munich
Ng A (n.d.) Convolutional neural networks [MOOC]. Coursera. https://www.coursera.org/learn/convolutional-neural-networks
Caesar H et al (2019) nuScenes: a multimodal dataset for autonomous driving. arXiv:1903.11027. Available http://arxiv.org/abs/1903.11027
Sheeny M, Wallace A, Wang S (2020) RADIO: parameterized generative radar data augmentation for small datasets. Appl Sci 10(11):3861
Han H et al (2019) Object classification on raw radar data using convolutional neural networks. IEEE Sens Appl Symp (SAS) 2019:1–6. https://doi.org/10.1109/SAS.2019.8706004
Hershkovitz H (2020) Augmenting radar data with shifts & flips,” medium, 18 Nov 2020 [online]. Available https://medium.com/definitely-not-sota-but-we-do-our-best/augmenting-radar-data-with-shifts-flips-81beb857e705. Accessed 26 Oct 2021
Ng A (n.d.) Machine learning [MOOC]. Coursera. https://www.coursera.org/learn/machine-learning
Coordinate geometry basics-rotation of axes. DoubleRoot.in, 24 Dec 2020 [online]. Available https://doubleroot.in/lessons/coordinate-geometry-basics/rotation-of-axes/. Accessed 03 Nov 2021
CameraRadarFusionNet, Feb 2, 2020 [online]. Available https://github.com/TUMFTM/CameraRadarFusionNet. Accessed 11/03/2021
Weber M (2019) Autonomous driving: radar sensor noise filtering and multimodal sensor fusion for object detection with artificial neural networks. Master’s thesis, Technical University of Munich
nuscenes-devkit, Oct 9, 2019 [online]. Available https://github.com/Fellfalla/nuscenes-devkit/tree/1e1b3d6320d7d9b0eca05969a316b0bd747d7e95. Accessed 11/03/2021
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Prasanna, S., El-Sharkawy, M. (2023). Improving Mean Average Precision (mAP) of Camera and Radar Fusion Network for Object Detection Using Radar Augmentation. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Seventh International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 465. Springer, Singapore. https://doi.org/10.1007/978-981-19-2397-5_6
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DOI: https://doi.org/10.1007/978-981-19-2397-5_6
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