![]() ![]() Denton et al., 2015 Denton E.L., Chintala S., Fergus R., et al., Deep generative image models using a Laplacian pyramid of adversarial networks, in: Advances in Neural Information Processing Systems, 2015, pp.Deng et al., 2009 Deng J., Dong W., Socher R., Li L.J., Li K., Fei-Fei L., Imagenet: A large-scale hierarchical image database, in: Computer Vision and Pattern Recognition, 2009 CVPR 2009 IEEE Conference on, IEEE, 2009, pp.The cityscapes dataset for semantic urban scene understanding, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. Cordts et al., 2016 Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B., 2016.Cho et al., 2014 Cho H., Seo Y.W., Kumar B.V., Rajkumar R.R., A multi-sensor fusion system for moving object detection and tracking in urban driving environments, in: Robotics and Automation (ICRA), 2014 IEEE International Conference on, IEEE, 2014, pp. ![]() Chen et al., 2018 Chen L.C., Papandreou G., Kokkinos I., Murphy K., Yuille A.L., Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, IEEE Trans.Chen et al., 2016 Chen X., Duan Y., Houthooft R., Schulman J., Sutskever I., Abbeel P., Infogan: Interpretable representation learning by information maximizing generative adversarial nets, in: Advances in Neural Information Processing Systems, 2016, pp.Badrinarayanan et al., 2017 Badrinarayanan V., Kendall A., Cipolla R., Segnet: A deep convolutional encoder–decoder architecture for image segmentation, IEEE Trans.Arjovsky et al., 2017 Arjovsky M., Chintala S., Bottou L., Wasserstein gan, 2017, arXiv preprint arXiv:1701.07875.The essential point of the work is the proposal of a novel Conditional Multi-Generator Generative Adversarial Network (CMGGAN) that, being conditioned on the radar sensor measurements, can produce visually appealing images that qualitatively and quantitatively contain all environment features detected by the radar sensor. Through such data fusion, the algorithm produces more consistent, accurate, and useful information than that provided solely by the radar or the camera. A proposed fully-unsupervised machine learning algorithm converts the radar sensor data to artificial, camera-like, environmental images. This paper presents a method for fusing the radar sensor measurements with the camera images. On the other hand, without a large amount of high-quality labeled training data, it is difficult, if not impossible, to ensure that the supervised machine learning models can predict, classify, or otherwise analyze the phenomenon of interest with the required accuracy. Low sensor precision causes ambiguity in the human interpretation of the measurement data and makes the data labeling process difficult and expensive. However, radar sensors have considerably lower precision than cameras. Largely owing to this reputation, they have found broad application in driver assistance and highly automated driving systems. Radar sensors are considered to be very robust under harsh weather and poor lighting conditions. ![]()
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