Survey on Computer Vision for UAVs: Current Developments and Trends

Kanellakis, Christoforos; Nikolakopoulos, George

doi:10.1007/s10846-017-0483-z

Cited by 289 publications

(129 citation statements)

References 134 publications

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“…Recently, significant results have been achieved by Deep Neural Networks (DNN) in the task of pose estimation based on monocular imagery. In this sense, the use of a Convolutional Neural Network (CNN) [6] to learn and to match features, which aids in camera pose estimation, has become popular with the work of Kendall et al [12] and more recently the work of Mueller et al [30]. However, both approaches rely on prior environmental knowledge before yielding an estimation of the camera position.…”

Section: Related Workmentioning

confidence: 99%

Multi-Task Regression-Based Learning for Autonomous Unmanned Aerial Vehicle Flight Control Within Unstructured Outdoor Environments

Maciel-Pearson

Akçay

Atapour-Abarghouei

et al. 2019

IEEE Robot. Autom. Lett.

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Increased growth in the global Unmanned Aerial Vehicles (UAV) (drone) industry has expanded possibilities for fully autonomous UAV applications. A particular application which has in part motivated this research is the use of UAV in wide area search and surveillance operations in unstructured outdoor environments. The critical issue with such environments is the lack of structured features that could aid in autonomous flight, such as road lines or paths. In this paper, we propose an End-to-End Multi-Task Regression-based Learning approach capable of defining flight commands for navigation and exploration under the forest canopy, regardless of the presence of trails or additional sensors (i.e. GPS). Training and testing are performed using a software in the loop pipeline which allows for a detailed evaluation against state-of-the-art pose estimation techniques.Our extensive experiments demonstrate that our approach excels in performing dense exploration within the required search perimeter, is capable of covering wider search regions, generalises to previously unseen and unexplored environments and outperforms contemporary state-of-the-art techniques.

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Section: Related Workmentioning

confidence: 99%

Multi-Task Regression-Based Learning for Autonomous Unmanned Aerial Vehicle Flight Control Within Unstructured Outdoor Environments

Maciel-Pearson

Akçay

Atapour-Abarghouei

et al. 2019

IEEE Robot. Autom. Lett.

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show abstract

“…For example, it can guide unmanned aerial vehicles (UAV) for autonomous landing. UAV is increasingly used in target tracking, danger rescue and other tasks, which requires higher and higher guidance efficiency and accuracy [75,76]. Like manned aircraft, the mission consists of three stages: take-off, cruise, and landing.…”

Section: Compare With the Relevant Qr Location Algorithmmentioning

confidence: 99%

“…Visual navigation based on cooperative target is a reliable method for autonomous landing of UAV [74][75][76][77][78][79][80][81][82][83]. Accurate positioning of the cooperative target is the basis of autonomous landing system.…”

Section: Compare With the Relevant Qr Location Algorithmmentioning

confidence: 99%

MU R-CNN: A Two-Dimensional Code Instance Segmentation Network Based on Deep Learning

Yuan

Li²,

Jiang

et al. 2019

Future Internet

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In the context of Industry 4.0, the most popular way to identify and track objects is to add tags, and currently most companies still use cheap quick response (QR) tags, which can be positioned by computer vision (CV) technology. In CV, instance segmentation (IS) can detect the position of tags while also segmenting each instance. Currently, the mask region-based convolutional neural network (Mask R-CNN) method is used to realize IS, but the completeness of the instance mask cannot be guaranteed. Furthermore, due to the rich texture of QR tags, low-quality images can lower intersection-over-union (IoU) significantly, disabling it from accurately measuring the completeness of the instance mask. In order to optimize the IoU of the instance mask, a QR tag IS method named the mask UNet region-based convolutional neural network (MU R-CNN) is proposed. We utilize the UNet branch to reduce the impact of low image quality on IoU through texture segmentation. The UNet branch does not depend on the features of the Mask R-CNN branch so its training process can be carried out independently. The pre-trained optimal UNet model can ensure that the loss of MU R-CNN is accurate from the beginning of the end-to-end training. Experimental results show that the proposed MU R-CNN is applicable to both high-and low-quality images, and thus more suitable for Industry 4.0.

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“…Most existing systems rely exclusively on visual servoing (cameras, stereo-cameras) and SLAM techniques (DJI, 2017;Kanellakis, 2017;Sabatini, 2013;Burri, 2015;Fallavollita, 2012). These systems are very good when operating in favorable light and environment conditions, but suffer with certain kind of obstacles like wires and nets, and operation in poor light conditions like night-time or foggy scenarios.…”

Section: State Of the Artmentioning

confidence: 99%

Obstacle Detection System Involving Fusion of Multiple Sensor Technologies

Giannì¹,

Balsi²,

Esposito³

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Obstacle detection is a fundamental task for Unmanned Aerial Vehicles (UAV) as a part of a Sense and Avoid system. In this study, we present a method of multi-sensor obstacle detection that demonstrated good results on different kind of obstacles. This method can be implemented on low-cost platforms involving a DSP or small FPGA. In this paper, we also present a study on the typical targets that can be tough to detect because of their characteristics of reflectivity, form factor, heterogeneity and show how data fusion can often overcome the limitations of each technology.

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Survey on Computer Vision for UAVs: Current Developments and Trends

Cited by 289 publications

References 134 publications

Multi-Task Regression-Based Learning for Autonomous Unmanned Aerial Vehicle Flight Control Within Unstructured Outdoor Environments

Multi-Task Regression-Based Learning for Autonomous Unmanned Aerial Vehicle Flight Control Within Unstructured Outdoor Environments

MU R-CNN: A Two-Dimensional Code Instance Segmentation Network Based on Deep Learning

Obstacle Detection System Involving Fusion of Multiple Sensor Technologies

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