Deep Learning Based Drone Detection and Classification

Yi, Kyu Yang; Kyeong, Deokhwan; Seo, Kisung

doi:10.5370/kiee.2019.68.2.359

Cited by 4 publications

(1 citation statement)

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“…2 shows the distributed heat map of the target bounding box sizes in all training samples. The sizes of the targets are relatively fixed, mainly concentrated in the range of W ∈ [15,32] and H ∈ [8,18]. There are also some targets settled in the range of W ∈ [33,46] and H ∈ [19,29], but the number is relatively small.…”

Section: B Multi-scale Feature Map Fusion Via Dense Lateral Connectionsmentioning

confidence: 99%

Design and Training of Deep CNN-Based Fast Detector in Infrared SUAV Surveillance System

Zhang

Shi

et al. 2019

IEEE Access

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Real-time detection of small unmanned aerial vehicle (SUAV) targets in SUAV surveillance systems has become a challenge due to their high mobility, sudden bursts, and small sizes. In this study, we used infrared sensors and Convolutional Neural Networks (CNN)-based detectors to achieve the real-time detection of SUAV targets. Existing object detectors generally suffer from a computational burden or low detection accuracy on small targets, which limits their practicality and further application in SUAV surveillance systems. To solve these problems, we developed a real-time SUAV target detection algorithm based on deep residual networks. In order to improve the sensitivity to small targets, a laterally connected multi-scale feature fusion approach was proposed to fully combine the context features and semantic features. A densely paved pre-defined box with geometric analysis was used for single-stage prediction. Compared with the state-of-the-art object detectors, the proposed method achieved superior performance with respect to average-precision and frames-per-second. As the training set was limited, to improve generalization, we investigate the benefits introduced by data augmentation and data balance, and proposed a weighted augmentation approach. The proposed approach improved the robustness of the detector and the overall accuracy.INDEX TERMS Small unmanned aerial vehicle (SUAV) surveillance, infrared target detection, real-time detection, data augmentation.

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Section: B Multi-scale Feature Map Fusion Via Dense Lateral Connectionsmentioning

confidence: 99%

Design and Training of Deep CNN-Based Fast Detector in Infrared SUAV Surveillance System

Zhang

Shi

et al. 2019

IEEE Access

View full text Add to dashboard Cite

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AI Emmbedded in Drone Control

Caballero-Martin,

Lopez-Guede,

Estevez

et al. 2024

Lecture Notes in Computer Science

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Artificial Intelligence Applied to Drone Control: A State of the Art

Caballero-Martin,

Lopez-Guede,

Estevez

et al. 2024

Drones

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The integration of Artificial Intelligence (AI) tools and techniques has provided a significant advance in drone technology. Besides the military applications, drones are being increasingly used for logistics and cargo transportation, agriculture, construction, security and surveillance, exploration, and mobile wireless communication. The synergy between drones and AI has led to notable progress in the autonomy of drones, which have become capable of completing complex missions without direct human supervision. This study of the state of the art examines the impact of AI on improving drone autonomous behavior, covering from automation to complex real-time decision making. The paper provides detailed examples of the latest developments and applications. Ethical and regulatory challenges are also considered for the future evolution of this field of research, because drones with AI have the potential to greatly change our socioeconomic landscape.

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Deep Learning Based Drone Detection and Classification

Cited by 4 publications

References 0 publications

Design and Training of Deep CNN-Based Fast Detector in Infrared SUAV Surveillance System

Design and Training of Deep CNN-Based Fast Detector in Infrared SUAV Surveillance System

AI Emmbedded in Drone Control

Artificial Intelligence Applied to Drone Control: A State of the Art

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