publication . Conference object . Preprint . 2017

DroNet: Efficient convolutional neural network detector for real-time UAV applications

Christos Kyrkou; George Plastiras; Theocharis Theocharides; Stylianos I. Venieris; Christos-Savvas Bouganis;
Open Access English
  • Published: 10 Nov 2017
  • Publisher: IEEE
  • Country: United Kingdom
Abstract
Unmanned Aerial Vehicles (drones) are emerging as a promising technology for both environmental and infrastructure monitoring, with broad use in a plethora of applications. Many such applications require the use of computer vision algorithms in order to analyse the information captured from an on-board camera. Such applications include detecting vehicles for emergency response and traffic monitoring. This paper therefore, explores the trade-offs involved in the development of a single-shot object detector based on deep convolutional neural networks (CNNs) that can enable UAVs to perform vehicle detection under a resource constrained environment such as in a UAV....
Persistent Identifiers
Subjects
ACM Computing Classification System: ComputerApplications_COMPUTERSINOTHERSYSTEMSComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Detectors, Convolutional neural networks, Real-time systems, Training, Computer architecture, Machine learning, Graphics processing units, Science & Technology, Technology, Automation & Control Systems, Computer Science, Theory & Methods, Engineering, Electrical & Electronic, Computer Science, Engineering, cs.CV, Computer Science - Computer Vision and Pattern Recognition, Architecture, Software deployment, Detector, Data collection, Drone, Computer vision algorithms, Real-time computing, Embedded processing, Computer science, Convolutional neural network
Funded by
EC| KIOS CoE
Project
KIOS CoE
KIOS Research and Innovation Centre of Excellence
  • Funder: European Commission (EC)
  • Project Code: 739551
  • Funding stream: H2020 | SGA-CSA
Validated by funder

[1] L. Cavigelli, M. Magno, and L. Benini, “Accelerating Real-Time Embedded Scene Labeling with Convolutional Networks,” in Design Automation Conference (DAC). ACM, 2015, pp. 1-6.

[2] A. De Bruin and M. J. Booysen, “Drone-based traffic flow estimation and tracking using computer vision,” 2015. [OpenAIRE]

[3] C. L. Azevedo, J. L. Cardoso, M. Ben-Akiva, J. P. Costeira, and M. Marques, “Automatic vehicle trajectory extraction by aerial remote sensing,” Procedia - Social and Behavioral Sciences, vol. 111, pp. 849 - 858, 2014, transportation: Can we do more with less resources? ? 16th Meeting of the Euro Working Group on Transportation ? Porto 2013. [Online]. Available: http: //www.sciencedirect.com/science/article/pii/S1877042814001207

[4] C. Szegedy, A. Toshev, and D. Erhan, “Deep neural networks for object detection,” in Advances in Neural Information Processing Systems 26, C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2013, pp. 2553-2561. [Online]. Available: http://papers.nips.cc/paper/ 5207-deep-neural-networks-for-object-detection.pdf

[5] X. Chen, S. Xiang, C. L. Liu, and C. H. Pan, “Vehicle detection in satellite images by parallel deep convolutional neural networks,” in 2013 2nd IAPR Asian Conference on Pattern Recognition, Nov 2013, pp. 181- 185.

[6] N. Audebert, B. Le Saux, and S. Lefvre, “Segment-before-detect: Vehicle detection and classification through semantic segmentation of aerial images,” Remote Sensing, vol. 9, no. 4, 2017. [Online]. Available: http://www.mdpi.com/2072-4292/9/4/368

[7] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2017.

Abstract
Unmanned Aerial Vehicles (drones) are emerging as a promising technology for both environmental and infrastructure monitoring, with broad use in a plethora of applications. Many such applications require the use of computer vision algorithms in order to analyse the information captured from an on-board camera. Such applications include detecting vehicles for emergency response and traffic monitoring. This paper therefore, explores the trade-offs involved in the development of a single-shot object detector based on deep convolutional neural networks (CNNs) that can enable UAVs to perform vehicle detection under a resource constrained environment such as in a UAV....
Persistent Identifiers
Subjects
ACM Computing Classification System: ComputerApplications_COMPUTERSINOTHERSYSTEMSComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Detectors, Convolutional neural networks, Real-time systems, Training, Computer architecture, Machine learning, Graphics processing units, Science & Technology, Technology, Automation & Control Systems, Computer Science, Theory & Methods, Engineering, Electrical & Electronic, Computer Science, Engineering, cs.CV, Computer Science - Computer Vision and Pattern Recognition, Architecture, Software deployment, Detector, Data collection, Drone, Computer vision algorithms, Real-time computing, Embedded processing, Computer science, Convolutional neural network
Funded by
EC| KIOS CoE
Project
KIOS CoE
KIOS Research and Innovation Centre of Excellence
  • Funder: European Commission (EC)
  • Project Code: 739551
  • Funding stream: H2020 | SGA-CSA
Validated by funder

[1] L. Cavigelli, M. Magno, and L. Benini, “Accelerating Real-Time Embedded Scene Labeling with Convolutional Networks,” in Design Automation Conference (DAC). ACM, 2015, pp. 1-6.

[2] A. De Bruin and M. J. Booysen, “Drone-based traffic flow estimation and tracking using computer vision,” 2015. [OpenAIRE]

[3] C. L. Azevedo, J. L. Cardoso, M. Ben-Akiva, J. P. Costeira, and M. Marques, “Automatic vehicle trajectory extraction by aerial remote sensing,” Procedia - Social and Behavioral Sciences, vol. 111, pp. 849 - 858, 2014, transportation: Can we do more with less resources? ? 16th Meeting of the Euro Working Group on Transportation ? Porto 2013. [Online]. Available: http: //www.sciencedirect.com/science/article/pii/S1877042814001207

[4] C. Szegedy, A. Toshev, and D. Erhan, “Deep neural networks for object detection,” in Advances in Neural Information Processing Systems 26, C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2013, pp. 2553-2561. [Online]. Available: http://papers.nips.cc/paper/ 5207-deep-neural-networks-for-object-detection.pdf

[5] X. Chen, S. Xiang, C. L. Liu, and C. H. Pan, “Vehicle detection in satellite images by parallel deep convolutional neural networks,” in 2013 2nd IAPR Asian Conference on Pattern Recognition, Nov 2013, pp. 181- 185.

[6] N. Audebert, B. Le Saux, and S. Lefvre, “Segment-before-detect: Vehicle detection and classification through semantic segmentation of aerial images,” Remote Sensing, vol. 9, no. 4, 2017. [Online]. Available: http://www.mdpi.com/2072-4292/9/4/368

[7] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2017.

Any information missing or wrong?Report an Issue