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Open AccessArticle

Real-Time Vehicle-Detection Method in Bird-View Unmanned-Aerial-Vehicle Imagery

1
Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea
2
Department of Smart Convergence, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(18), 3958; https://doi.org/10.3390/s19183958
Received: 31 July 2019 / Revised: 5 September 2019 / Accepted: 10 September 2019 / Published: 13 September 2019
Vehicle detection is an important research area that provides background information for the diversity of unmanned-aerial-vehicle (UAV) applications. In this paper, we propose a vehicle-detection method using a convolutional-neural-network (CNN)-based object detector. We design our method, DRFBNet300, with a Deeper Receptive Field Block (DRFB) module that enhances the expressiveness of feature maps to detect small objects in the UAV imagery. We also propose the UAV-cars dataset that includes the composition and angular distortion of vehicles in UAV imagery to train our DRFBNet300. Lastly, we propose a Split Image Processing (SIP) method to improve the accuracy of the detection model. Our DRFBNet300 achieves 21 mAP with 45 FPS in the MS COCO metric, which is the highest score compared to other lightweight single-stage methods running in real time. In addition, DRFBNet300, trained on the UAV-cars dataset, obtains the highest AP score at altitudes of 20–50 m. The gap of accuracy improvement by applying the SIP method became larger when the altitude increases. The DRFBNet300 trained on the UAV-cars dataset with SIP method operates at 33 FPS, enabling real-time vehicle detection. View Full-Text
Keywords: vehicle detection; object detection; UAV imagery; convolutional neural network vehicle detection; object detection; UAV imagery; convolutional neural network
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MDPI and ACS Style

Han, S.; Yoo, J.; Kwon, S. Real-Time Vehicle-Detection Method in Bird-View Unmanned-Aerial-Vehicle Imagery. Sensors 2019, 19, 3958.

AMA Style

Han S, Yoo J, Kwon S. Real-Time Vehicle-Detection Method in Bird-View Unmanned-Aerial-Vehicle Imagery. Sensors. 2019; 19(18):3958.

Chicago/Turabian Style

Han, Seongkyun; Yoo, Jisang; Kwon, Soonchul. 2019. "Real-Time Vehicle-Detection Method in Bird-View Unmanned-Aerial-Vehicle Imagery" Sensors 19, no. 18: 3958.

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