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Acquiring data to train deep learning-based object detectors on Unmanned Aerial Vehicles (UAVs) is expensive, time-consuming and may even be prohibited by law in specific environments.
J. Hoffman, E. Tzeng, T. Park, J.-Y. Zhu, P. Isola, K. Saenko, A. Efros, and T. Darrell, “Cycada: Cycle-consistent adversarial domain adaptation,” in International conference on machine learning . PMLR, 2018, pp. 1989–1998
1998
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
J. C. van Gemert, C. R. Verschoor, P. Mettes, K. Epema, L. P. Koh, and S. Wich, “Nature conservation drones for automatic localization and counting of animals,” in European Conference on Computer Vision . Springer, 2014, pp. 255–270
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European conference on computer vision . Springer, 2014, pp. 740–755
2014
Earlier work this paper cites.
2016
Earlier work this paper cites.
W. Qiu and A. Yuille, “Unrealcv: Connecting computer vision to unreal engine,” in European Conference on Computer Vision . Springer, 2016, pp. 909–916
2016
Earlier work this paper cites.
M. Mueller, N. Smith, and B. Ghanem, “A benchmark and simulator for uav tracking,” in European conference on computer vision . Springer, 2016, pp. 445–461
2016
Earlier work this paper cites.
T. N. Mundhenk, G. Konjevod, W. A. Sakla, and K. Boakye, “A large contextual dataset for classification, detection and counting of cars with deep learning,” in European Conference on Computer Vision . Springer, 2016, pp. 785–800
2016
Earlier work this paper cites.
F. Ofli, P. Meier, M. Imran, C. Castillo, D. Tuia, N. Rey, J. Briant, P. Millet, F. Reinhard, M. Parkan et al. , “Combining human computing and machine learning to make sense of big (aerial) data for disaster response,” Big data , vol. 4, no. 1, pp. 47–59, 2016
2016
Earlier work this paper cites.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 779–788
2016
Earlier work this paper cites.
T. Adão, J. Hruška, L. Pádua, J. Bessa, E. Peres, R. Morais, and J. J. Sousa, “Hyperspectral imaging: A review on uav-based sensors, data processing and applications for agriculture and forestry,” Remote Sensing , vol. 9, no. 11, p. 1110, 2017
2017
Earlier work this paper cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” in Conference on robot learning . PMLR, 2017, pp. 1–16
2017
Earlier work this paper cites.
M.-R. Hsieh, Y.-L. Lin, and W. H. Hsu, “Drone-based object counting by spatially regularized regional proposal network,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 4145–4153
2017
Earlier work this paper cites.
S. Li and D.-Y. Yeung, “Visual object tracking for unmanned aerial vehicles: A benchmark and new motion models,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 31, no. 1, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” in 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2017, pp. 23–30
2017
Earlier work this paper cites.
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2223–2232
2017
Earlier work this paper cites.
J. Redmon and A. Farhadi, “YOLO9000: better, faster, stronger,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 7263–7271
2017
Earlier work this paper cites.
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1492–1500
2017
Earlier work this paper cites.
G. Varol, J. Romero, X. Martin, N. Mahmood, M. J. Black, I. Laptev, and C. Schmid, “Learning from synthetic humans,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 109–117
2017
Cited alongside, same era.
K. T. San, S. J. Mun, Y. H. Choe, and Y. S. Chang, “Uav delivery monitoring system,” in MATEC Web of Conferences , vol. 151. EDP Sciences, 2018, p. 04011
2018
Cited alongside, same era.
P. Zhu, L. Wen, D. Du, X. Bian, H. Ling, Q. Hu, Q. Nie, H. Cheng, C. Liu, X. Liu et al. , “Visdrone-det2018: The vision meets drone object detection in image challenge results,” in Proceedings of the European Conference on Computer Vision (ECCV) Workshops , 2018, pp. 0–0
2018
Cited alongside, same era.
D. Du, Y. Qi, H. Yu, Y. Yang, K. Duan, G. Li, W. Zhang, Q. Huang, and Q. Tian, “The unmanned aerial vehicle benchmark: Object detection and tracking,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 370–386
2018
B. Hurl, K. Czarnecki, and S. Waslander, “Precise synthetic image and lidar (presil) dataset for autonomous vehicle perception,” in 2019 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2019, pp. 2522–2529
2019
Later among the works it cites.
M. Fonder and M. V. Droogenbroeck, “Mid-air: A multi-modal dataset for extremely low altitude drone flights,” in Conference on Computer Vision and Pattern Recognition Workshop (CVPRW) , June 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
Z. Pei, X. Qi, Y. Zhang, M. Ma, and Y.-H. Yang, “Human trajectory prediction in crowded scene using social-affinity long short-term memory,” Pattern Recognition , vol. 93, pp. 273–282, 2019
2019
Later among the works it cites.
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Cited alongside, same era.
M. Angus, M. ElBalkini, S. Khan, A. Harakeh, O. Andrienko, C. Reading, S. Waslander, and K. Czarnecki, “Unlimited road-scene synthetic annotation (ursa) dataset,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2018, pp. 985–992
2018
Cited alongside, same era.
X. Yue, B. Wu, S. A. Seshia, K. Keutzer, and A. L. Sangiovanni-Vincentelli, “A lidar point cloud generator: from a virtual world to autonomous driving,” in Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval , 2018, pp. 458–464
2018
Cited alongside, same era.
S. Shah, D. Dey, C. Lovett, and A. Kapoor, “Airsim: High-fidelity visual and physical simulation for autonomous vehicles,” in Field and service robotics . Springer, 2018, pp. 621–635
2018
Cited alongside, same era.
R. Krajewski, J. Bock, L. Kloeker, and L. Eckstein, “The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2018, pp. 2118–2125
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Y. Zou, Z. Yu, B. Kumar, and J. Wang, “Unsupervised domain adaptation for semantic segmentation via class-balanced self-training,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 289–305
2018
Cited alongside, same era.
Y. Chen, W. Li, C. Sakaridis, D. Dai, and L. Van Gool, “Domain adaptive faster r-cnn for object detection in the wild,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 3339–3348
2018
Cited alongside, same era.
Y.-H. Tsai, W.-C. Hung, S. Schulter, K. Sohn, M.-H. Yang, and M. Chandraker, “Learning to adapt structured output space for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7472–7481
2018
Cited alongside, same era.
A. Prakash, S. Boochoon, M. Brophy, D. Acuna, E. Cameracci, G. State, O. Shapira, and S. Birchfield, “Structured domain randomization: Bridging the reality gap by context-aware synthetic data,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 7249–7255
2019
Later among the works it cites.
H. Fan, L. Wen, D. Du, P. Zhu, Q. Hu, H. Ling, M. Shah, B. Wang, B. Dong, D. Yuan et al. , “Visdrone-sot2020: The vision meets drone single object tracking challenge results,” in European Conference on Computer Vision . Springer, 2020, pp. 728–749
2020
Later among the works it cites.
I. Bozcan and E. Kayacan, “Au-air: A multi-modal unmanned aerial vehicle dataset for low altitude traffic surveillance,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 8504–8510
2020
Later among the works it cites.
F. Kong, B. Huang, K. Bradbury, and J. Malof, “The synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2020, pp. 1814–1823
2020
Later among the works it cites.
W. Shao, R. Kawakami, R. Yoshihashi, S. You, H. Kawase, and T. Naemura, “Cattle detection and counting in uav images based on convolutional neural networks,” International Journal of Remote Sensing , vol. 41, no. 1, pp. 31–52, 2020
2020
Later among the works it cites.
M. Tan, R. Pang, and Q. V. Le, “Efficientdet: Scalable and efficient object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 10 781–10 790
2020
Later among the works it cites.
2020
Later among the works it cites.
G. Jocher, L. Changyu, A. Hogan, L. Yu, changyu98, P. Rai, and T. Sullivan, “ultralytics/yolov5: Initial Release,” Jun. 2020. [Online]. Available: https://doi.org/10.5281/zenodo.3908560
2020
Later among the works it cites.
S. GmbH, “A pytorch implementation of efficientdet object detection,” https://github.com/signatrix/efficientdet , 2020
2020
Later among the works it cites.
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G. Jocher, A. Stoken, J. Borovec, NanoCode012, A. Chaurasia, TaoXie, L. Changyu, A. V, Laughing, tkianai, yxNONG, A. Hogan, lorenzomammana, AlexWang1900, J. Hajek, L. Diaconu, Marc, Y. Kwon, oleg, wanghaoyang0106, Y. Defretin, A. Lohia, ml5ah, B. Milanko, B. Fineran, D. Khromov, D. Yiwei, Doug, Durgesh, and F. Ingham, “ultralytics/yolov5: v5.0 - YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations,” Apr. 2021. [Online]. Available: https://doi.org/10.5281/zenodo.4679653
2021
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