27 April 2018 A real-time object detection framework for aerial imagery using deep neural networks and synthetic training images
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Abstract
Efficient and accurate real-time perception systems are critical for Unmanned Aerial Vehicle (UAV) applications that aim to provide enhanced situational awareness to users. Specifically, object recognition is a crucial element for surveillance and reconnaissance missions since it provides fundamental semantic information of the aerial scene. In this study, we describe the development and implementation of a perception frame-work on an embedded computer vision platform, mounted on a hexacopter for real-time object detection. The framework includes a camera driver and a deep neural network based object detection module and has distributed computing capabilities between the aerial platform and the corresponding ground station. Preliminary aerial real-time object detections using YOLO are performed onboard a UAV and a sequence of images are streamed to the base station where an advanced computer vision algorithm, referred to as Multi-Expert Region-based CNN (ME- RCNN), is leveraged to provide enhanced and fine-grained analytics on the aerial video feeds. Since annotated aerial imagery in the UAV domain is hard to obtain and not routinely available, we use a combination of aerial data as well as air-to-ground synthetic images, such as vehicles, generated by video gaming engines for training the neural network. Through this study, we quantify the level of improvements with the use of the synthetic dataset and the efficacy of using advanced object detection algorithms.
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Priya Narayanan, Priya Narayanan, Christoph Borel-Donohue, Christoph Borel-Donohue, Hyungtae Lee, Hyungtae Lee, Heesung Kwon, Heesung Kwon, Raghuveer Rao, Raghuveer Rao, } "A real-time object detection framework for aerial imagery using deep neural networks and synthetic training images", Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 1064614 (27 April 2018); doi: 10.1117/12.2306154; https://doi.org/10.1117/12.2306154
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