publication . Part of book or chapter of book . Other literature type . Article . Conference object . Preprint . 2019

Application in Unmanned Aerial Vehicles

Kamilaris, Andreas; van den Brink, Corjan; Karatsiolis, Savvas; Vento, Mario; Percannella, Gennaro; Colantonio, Sara; Giorgi, Daniela; Matuszewski, Bogdan J.; Kerdegari, Hamideh; Razaak, Manzoor;
Open Access
  • Published: 30 Oct 2019
  • Publisher: Zenodo
  • Country: Netherlands
Abstract
Comment: Workshop on Deep-learning based computer vision for UAV in conjunction with CAIP 2019, Salerno, italy, September 2019
Subjects
ACM Computing Classification System: ComputerApplications_COMPUTERSINOTHERSYSTEMS
free text keywords: UAV Deep Learning, Generative Data, UAV, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Synthetic data, Imagery analysis, Machine learning, computer.software_genre, computer, Training set, Deep learning, Artificial intelligence, business.industry, business, Counting problem, Aerial imagery, Computer science, Aerial photos
Funded by
EC| RISE
Project
RISE
Research Center on Interactive Media, Smart System and Emerging Technologies
  • Funder: European Commission (EC)
  • Project Code: 739578
  • Funding stream: H2020 | SGA-CSA
Validated by funder
Download fromView all 8 versions
http://arxiv.org/pdf/1908.0647...
Part of book or chapter of book
Provider: UnpayWall
Universiteit Twente Repository
Conference object . 2019
Provider: NARCIS
ZENODO
Conference object . 2019
Provider: ZENODO
26 references, page 1 of 2

1. Amara, J., Bouaziz, B., Algergawy, A., et al.: A deep learning-based approach for banana leaf diseases classi cation. In: BTW (Workshops). pp. 79{88 (2017)

2. Canziani, A., Paszke, A., Culurciello, E.: An analysis of deep neural network models for practical applications. arXiv preprint arXiv:1605.07678 (2016) [OpenAIRE]

3. Douarre, C., Schielein, R., Frindel, C., Gerth, S., Rousseau, D.: Deep learning based root-soil segmentation from x-ray tomography. bioRxiv p. 071662 (2016) [OpenAIRE]

4. Dyrmann, M., Mortensen, A.K., Midtiby, H.S., Jorgensen, R.N., et al.: Pixel-wise classi cation of weeds and crops in images by using a fully convolutional neural network. In: Proceedings of the International Conference on Agricultural Engineering, Aarhus, Denmark. pp. 26{29 (2016)

5. Gaidon, A., Wang, Q., Cabon, Y., Vig, E.: Virtual worlds as proxy for multi-object tracking analysis. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4340{4349 (2016) [OpenAIRE]

6. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in neural information processing systems. pp. 2672{2680 (2014)

7. Kamilaris, A.: Simulating training data for deep learning models. In: Machine Learning in the Environmental Sciences Workshop, in Proc. of EnviroInfo 2018. Munich, Germany (September 2018) [OpenAIRE]

8. Kamilaris, A., Assumpcio, A., Blasi, A.B., Torrellas, M., Prenafeta-Boldu, F.X.: Estimating the environmental impact of agriculture by means of geospatial and big data analysis: The case of catalonia. In: Proc. of EnviroInfo. Luxembourg (September 2017)

9. Kamilaris, A., Prenafeta-Boldu, F.X.: Disaster monitoring using unmanned aerial vehicles and deep learning. In: Disaster Management for Resilience and Public Safety Workshop, in Proc. of EnviroInfo2017. Luxembourg (September 2017)

10. Kamilaris, A., Prenafeta-Boldu, F.X.: Deep learning in agriculture: A survey. Computers and Electronics in Agriculture 147, 70{90 (2018) [OpenAIRE]

11. Kar, A., Prakash, A., Liu, M.Y., Cameracci, E., Yuan, J., Rusiniak, M., Acuna, D., Torralba, A., Fidler, S.: Meta-sim: Learning to generate synthetic datasets. arXiv preprint arXiv:1904.11621 (2019)

12. Lehmussola, A., Ruusuvuori, P., Selinummi, J., Huttunen, H., Yli-Harja, O.: Computational framework for simulating uorescence microscope images with cell populations. IEEE transactions on medical imaging 26(7), 1010{1016 (2007)

13. Lempitsky, V., Zisserman, A.: Learning to count objects in images. In: Advances in neural information processing systems. pp. 1324{1332 (2010) [OpenAIRE]

14. Li, P., Liang, X., Jia, D., Xing, E.P.: Semantic-aware grad-gan for virtual-to-real urban scene adaption. arXiv preprint arXiv:1801.01726 (2018)

15. Olah, C., Satyanarayan, A., Johnson, I., Carter, S., Schubert, L., Ye, K., Mordvintsev, A.: The building blocks of interpretability. Distill 3(3), e10 (2018)

26 references, page 1 of 2
Abstract
Comment: Workshop on Deep-learning based computer vision for UAV in conjunction with CAIP 2019, Salerno, italy, September 2019
Subjects
ACM Computing Classification System: ComputerApplications_COMPUTERSINOTHERSYSTEMS
free text keywords: UAV Deep Learning, Generative Data, UAV, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Synthetic data, Imagery analysis, Machine learning, computer.software_genre, computer, Training set, Deep learning, Artificial intelligence, business.industry, business, Counting problem, Aerial imagery, Computer science, Aerial photos
Funded by
EC| RISE
Project
RISE
Research Center on Interactive Media, Smart System and Emerging Technologies
  • Funder: European Commission (EC)
  • Project Code: 739578
  • Funding stream: H2020 | SGA-CSA
Validated by funder
Download fromView all 8 versions
http://arxiv.org/pdf/1908.0647...
Part of book or chapter of book
Provider: UnpayWall
Universiteit Twente Repository
Conference object . 2019
Provider: NARCIS
ZENODO
Conference object . 2019
Provider: ZENODO
26 references, page 1 of 2

1. Amara, J., Bouaziz, B., Algergawy, A., et al.: A deep learning-based approach for banana leaf diseases classi cation. In: BTW (Workshops). pp. 79{88 (2017)

2. Canziani, A., Paszke, A., Culurciello, E.: An analysis of deep neural network models for practical applications. arXiv preprint arXiv:1605.07678 (2016) [OpenAIRE]

3. Douarre, C., Schielein, R., Frindel, C., Gerth, S., Rousseau, D.: Deep learning based root-soil segmentation from x-ray tomography. bioRxiv p. 071662 (2016) [OpenAIRE]

4. Dyrmann, M., Mortensen, A.K., Midtiby, H.S., Jorgensen, R.N., et al.: Pixel-wise classi cation of weeds and crops in images by using a fully convolutional neural network. In: Proceedings of the International Conference on Agricultural Engineering, Aarhus, Denmark. pp. 26{29 (2016)

5. Gaidon, A., Wang, Q., Cabon, Y., Vig, E.: Virtual worlds as proxy for multi-object tracking analysis. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4340{4349 (2016) [OpenAIRE]

6. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in neural information processing systems. pp. 2672{2680 (2014)

7. Kamilaris, A.: Simulating training data for deep learning models. In: Machine Learning in the Environmental Sciences Workshop, in Proc. of EnviroInfo 2018. Munich, Germany (September 2018) [OpenAIRE]

8. Kamilaris, A., Assumpcio, A., Blasi, A.B., Torrellas, M., Prenafeta-Boldu, F.X.: Estimating the environmental impact of agriculture by means of geospatial and big data analysis: The case of catalonia. In: Proc. of EnviroInfo. Luxembourg (September 2017)

9. Kamilaris, A., Prenafeta-Boldu, F.X.: Disaster monitoring using unmanned aerial vehicles and deep learning. In: Disaster Management for Resilience and Public Safety Workshop, in Proc. of EnviroInfo2017. Luxembourg (September 2017)

10. Kamilaris, A., Prenafeta-Boldu, F.X.: Deep learning in agriculture: A survey. Computers and Electronics in Agriculture 147, 70{90 (2018) [OpenAIRE]

11. Kar, A., Prakash, A., Liu, M.Y., Cameracci, E., Yuan, J., Rusiniak, M., Acuna, D., Torralba, A., Fidler, S.: Meta-sim: Learning to generate synthetic datasets. arXiv preprint arXiv:1904.11621 (2019)

12. Lehmussola, A., Ruusuvuori, P., Selinummi, J., Huttunen, H., Yli-Harja, O.: Computational framework for simulating uorescence microscope images with cell populations. IEEE transactions on medical imaging 26(7), 1010{1016 (2007)

13. Lempitsky, V., Zisserman, A.: Learning to count objects in images. In: Advances in neural information processing systems. pp. 1324{1332 (2010) [OpenAIRE]

14. Li, P., Liang, X., Jia, D., Xing, E.P.: Semantic-aware grad-gan for virtual-to-real urban scene adaption. arXiv preprint arXiv:1801.01726 (2018)

15. Olah, C., Satyanarayan, A., Johnson, I., Carter, S., Schubert, L., Ye, K., Mordvintsev, A.: The building blocks of interpretability. Distill 3(3), e10 (2018)

26 references, page 1 of 2
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