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 (1032 × 778 resolution) and 150 ms outdoors (1280 × 720 resolution) per frame, with a detection rate of 0.96 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.

Micro unmanned aerial vehicles (mUAV) became very common in recent years. As a result of their widespread usage, when they are flown by hobbyists illegally, crucial risks are imposed and such mUAVs need to be sensed by security systems. Furthermore, the sensing of mUAVs are essential for also swarm robotics research where the individuals in a flock of robots require systems to sense and localize each other for coordinated operation. In order to obtain such systems, there are studies to detect mUAVs utilizing different sensing mediums, such as vision, infrared and sound signals, and small-scale radars. However, there are still challenges that awaits to be handled in this field such as integrating tracking approaches to the vision-based detection systems to enhance accuracy and computational complexity. For this reason, in this study, we combine various tracking approaches to a vision-based mUAV detection system available in the literature, in order to evaluate different tracking approaches in terms of accuracy and as well as investigate the effect of such integration to the computational cost.