Vision-Based Detection and Distance Estimation of Micro Unmanned Aerial Vehicles

Sensors

Fatih Gök๾¹  Göktürk Üçoluk²  Erol ᗪhin²  Sinan Kalkan² 

[1] Department of Computer Engineering, Middle East Technical University, Üniversiteler Mahallesi, Dumlupınar Bulvarı No. 1, 06800 Çankaya Ankara, Turkey;

关键词: UAV;    micro UAV;    vision;    detection;    distance estimation;    cascaded classifiers;   

DOI  :  10.3390/s150923805

来源: mdpi

【 摘 要 】

Detection and distance estimation of micro unmanned aerial vehicles (mUAVs) is crucial for (i) the detection of intruder mUAVs in protected environments; (ii) sense and avoid purposes on mUAVs or on other aerial vehicles and (iii) multi-mUAV control scenarios, such as environmental monitoring, surveillance and exploration. In this article, we evaluate vision algorithms as alternatives for detection and distance estimation of mUAVs, since other sensing modalities entail certain limitations on the environment or on the distance. For this purpose, we test Haar-like features, histogram of gradients (HOG) and local binary patterns (LBP) using cascades of boosted classifiers. Cascaded boosted classifiers allow fast processing by performing detection tests at multiple stages, where only candidates passing earlier simple stages are processed at the preceding more complex stages. We also integrate a distance estimation method with our system utilizing geometric cues with support vector regressors. We evaluated each method on indoor and outdoor videos that are collected in a systematic way and also on videos having motion blur. Our experiments show that, using boosted cascaded classifiers with LBP, near real-time detection and distance estimation of mUAVs are possible in about 60 ms indoors ( F-score. However, the cascaded classifiers using Haar-like features lead to better distance estimation since they can position the bounding boxes on mUAVs more accurately. On the other hand, our time analysis yields that the cascaded classifiers using HOG train and run faster than the other algorithms.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.