Convolutional Neuronal Networks Based Monocular Object Detection and Depth Perception for Micro UAVs

Aguilar, Wilbert G.; Quisaguano, Fernando J.; Rodríguez, Guillermo A.; Álvarez, Leandro; Limaico, Alex; Sandoval, David

doi:10.1007/978-3-030-02698-1_35

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…Their experimental results on NYUD2 and SUN RGB-D datasets outperformed baselines. Aguilar et al created a real-time system for object detection and depth estimation using a micro-UAV's onboard camera and convolutional neural networks [151]. They emphasized that their approach avoids the need for complex SLAM visual systems, making it resourceefficient and faster.…”

Section: B Depth Perceptionmentioning

confidence: 99%

Synchronizing Object Detection: Applications, Advancements and Existing Challenges

Tanzib Hosain,

Zaman,

Abir

et al. 2024

IEEE Access

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From pivotal roles in autonomous vehicles, healthcare diagnostics, and surveillance systems to seamlessly integrating with augmented reality, object detection algorithms stand as the cornerstone in unraveling the complexities of the visual world. Tracing the trajectory from conventional region-based methods to the latest neural network architectures reveals a technological renaissance where algorithms metamorphose into digital artisans. However, this journey is not without hurdles, prompting researchers to grapple with real-time detection, robustness in varied environments, and interpretability amidst the intricacies of deep learning. The allure of addressing issues such as occlusions, scale variations, and finegrained categorization propels exploration into uncharted territories, beckoning the scholarly community to contribute to an ongoing saga of innovation and discovery. This research offers a comprehensive panorama, encapsulating the applications reshaping our digital reality, the advancements pushing the boundaries of perception, and the open issues extending an invitation to the next generation of visionaries to explore uncharted frontiers within object detection.

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Section: B Depth Perceptionmentioning

confidence: 99%

Synchronizing Object Detection: Applications, Advancements and Existing Challenges

Tanzib Hosain,

Zaman,

Abir

et al. 2024

IEEE Access

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“…W I ← Initial width 3: H I ← Initial height 4: (x min , x max , y min , y max ) ← Coordinates of the de- 5: tection bounding-box (multiplied by W I and H I ) 6:…”

Section: B Detection Algorithmmentioning

confidence: 99%

“…To obtain the depth of the stereo vision, the most effective approach is based on supervised learning in CNNs, which requires an abundant training set for a promising result applied in a single picture. Aguilar et al [6] proposed a novel unsupervised technique that first calculates disparity in an RGB image via CNNs and then depth by the geometrical relationship with disparity.…”

Section: Introductionmentioning

confidence: 99%

Target Detection, Tracking and Avoidance System for Low-cost UAVs using AI-Based Approaches

Varatharasan

Rao

Toutounji

et al. 2019

2019 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED UAS)

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An onboard target detection, tracking and avoidance system has been developed in this paper, for low-cost UAV flight controllers using AI-Based approaches. The aim of the proposed system is that an ally UAV can either avoid or track an unexpected enemy UAV with a net to protect itself. In this point of view, a simple and robust target detection, tracking and avoidance system is designed. Two open-source tools were used for the aim: a state-of-the-art object detection technique called SSD and an API for MAVLink compatible systems called MAVSDK. The MAVSDK performs velocity control when a UAV is detected so that the manoeuvre is done simply and efficiently. The proposed system was verified with Software in the loop (SITL) and Hardware in the loop (HITL) simulators. The simplicity of this algorithm makes it innovative, and therefore it should be used in future applications needing robust performances with low-cost hardware such as delivery drone applications.

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Monocular Vision-Based Dynamic Moving Obstacles Detection and Avoidance

Aguilar

Álvarez²,

Grijalva

et al. 2019

Intelligent Robotics and Applications

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Convolutional Neuronal Networks Based Monocular Object Detection and Depth Perception for Micro UAVs

Cited by 7 publications

References 23 publications

Synchronizing Object Detection: Applications, Advancements and Existing Challenges

Synchronizing Object Detection: Applications, Advancements and Existing Challenges

Target Detection, Tracking and Avoidance System for Low-cost UAVs using AI-Based Approaches

Monocular Vision-Based Dynamic Moving Obstacles Detection and Avoidance

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