Aircraft Detection using Deep Convolutional Neural Network in Small Unmanned Aircraft Systems

Hwang, Sangyeon; Lee, Jae-Hyun; Shin, Heemin; Cho, Sungwook; Shim, David Hyunchul

doi:10.2514/6.2018-2137

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…Consequently, scholars have shifted their attention towards developing systems that involve the pursuit of multiple UAVs [35]. High detection accuracy and a huge effective range make video-based recognition a powerful tool for detecting drones [36,37]. The supervised learning method used in this study is a strong computer vision tool and it is used to do detection on real-time data.…”

Section: Drone Swarmmentioning

confidence: 99%

Towards Reliable Identification and Tracking of Drones Within a Swarm

Kumari,

Lee,

Barca

et al. 2024

J Intell Robot Syst

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Drone swarms consist of multiple drones that can achieve tasks that individual drones can not, such as search and recovery or surveillance over a large area. A swarm’s internal structure typically consists of multiple drones operating autonomously. Reliable detection and tracking of swarms and individual drones allow a greater understanding of the behaviour and movement of a swarm. Increased understanding of drone behaviour allows better coordination, collision avoidance, and performance monitoring of individual drones in the swarm. The research presented in this paper proposes a deep learning-based approach for reliable detection and tracking of individual drones within a swarm using stereo-vision cameras in real time. The motivation behind this research is in the need to gain a deeper understanding of swarm dynamics, enabling improved coordination, collision avoidance, and performance monitoring of individual drones within a swarm. The proposed solution provides a precise tracking system and considers the highly dense and dynamic behaviour of drones. The approach is evaluated in both sparse and dense networks in a variety of configurations. The accuracy and efficiency of the proposed solution have been analysed by implementing a series of comparative experiments that demonstrate reasonable accuracy in detecting and tracking drones within a swarm.

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Section: Drone Swarmmentioning

confidence: 99%

Towards Reliable Identification and Tracking of Drones Within a Swarm

Kumari,

Lee,

Barca

et al. 2024

J Intell Robot Syst

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“…Hand-crafted features and classifiers methods also have been proposed by [16], [17]. The algorithm is deployed in the airplanes and must be robust to distinguish the airplanes or not in a limited power of computation.…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Wrecked Airplanes from UAV Images

Risnumawan

Perdana

Hidayatulloh

et al. 2019

emitter

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Searching the accident site of a missing airplane is the primary step taken by the search and rescue team before rescuing the victims. However, due to the vast exploration area, lack of technology, no access road, and rough terrain make the search process nontrivial and thus causing much delay in handling the victims. Therefore, this paper aims to develop an automatic wrecked airplane detection system using visual information taken from aerial images such as from a camera. A new deep network is proposed to distinguish robustly the wrecked airplane that has high pose, scale, color variation, and high deformable object. The network leverages the last layers to capture more abstract and semantics information for robust wrecked airplane detection. The network is intertwined by adding more extra layers connected at the end of the layers. To reduce missing detection which is crucial for wrecked airplane detection, an image is then composed into five patches going feed-forwarded to the net in a convolutional manner. Experiments show very well that the proposed method successfully reaches AP=91.87%, and we believe it could bring many benefits for the search and rescue team for accelerating the searching of wrecked airplanes and thus reducing the number of victims.

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“…In this paper we propose a system to address these concerns by exploiting aircraft visual appearance rather than relying on apparent motion with respect to the background. To exploit the visual appearance of aircraft, various machine learning [9] and deep learning [10], [11] approaches have been investigated for vision-based sense and avoid. In [9] a multi-stage detection pipeline is proposed which used a support vector machine (SVM) to exploit aircraft visual features.…”

Section: Introductionmentioning

confidence: 99%

“…The authors report an average precision of 75% on their UAV dataset and 79% on their aircraft dataset. In [11] the authors propose a deep CNN which is able to detect aircraft and achieved a 83% detection rate on the tested images with aircraft present. Recently [7] used a deep CNN fused with morphological processing to detect aircraft above the horizon with a mean detection range of 2527m and no false alarms.…”

Section: Introductionmentioning

confidence: 99%

Below Horizon Aircraft Detection Using Deep Learning for Vision-Based Sense and Avoid

James

Ford

Molloy

2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

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Commercial operation of unmanned aerial vehicles (UAVs) would benefit from an onboard ability to sense and avoid (SAA) potential mid-air collision threats. In this paper we present a new approach for detection of aircraft below the horizon. We address some of the challenges faced by existing vision-based SAA methods such as detecting stationary aircraft (that have no relative motion to the background), rejecting moving ground vehicles, and simultaneous detection of multiple aircraft. We propose a multi-stage, vision-based aircraft detection system which utilises deep learning to produce candidate aircraft that we track over time. We evaluate the performance of our proposed system on real flight data where we demonstrate detection ranges comparable to the state of the art with the additional capability of detecting stationary aircraft, rejecting moving ground vehicles, and tracking multiple aircraft.The authors are with the

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Aircraft Detection using Deep Convolutional Neural Network in Small Unmanned Aircraft Systems

Cited by 10 publications

References 18 publications

Towards Reliable Identification and Tracking of Drones Within a Swarm

Towards Reliable Identification and Tracking of Drones Within a Swarm

Automatic Detection of Wrecked Airplanes from UAV Images

Below Horizon Aircraft Detection Using Deep Learning for Vision-Based Sense and Avoid

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