Vision-Based Navigation Techniques for Unmanned Aerial Vehicles: Review and Challenges

Arafat, Muhammad Yeasir; Arafat, Muhammad Yeasir; Moh, Sangman

doi:10.3390/drones7020089

Cited by 93 publications

(33 citation statements)

References 116 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhao [18] introduced the YOLO target detection algorithm into the field of target detection, improved it on the basis of the YOLOv3 algorithm, and proposed the ST-YOLO algorithm for UAV target detection, which combined the TopHat transform with binary trees for detection of small targets for small target UAVs. However, due to the performance limitation of the YOLOv3 algorithm, the recognition effect of low-altitude UAVs is not ideal [19]. Ma [20] adds a residual network and multi-scale prediction on the basis of YOLOv3, uses the K-means clustering algorithm on the dataset of low-altitude UAVs to obtain the optimal anchor box, and fuses the residual network and the original network on the basis of the original YOLOv3 network to obtain a new O-YOLOv3 network, which makes the network easier to train and has a good recognition effect.…”

Section: Uav Target Recognition Algorithmmentioning

confidence: 99%

CA-LSTM: An Improved LSTM Trajectory Prediction Method Based on Infrared UAV Target Detection

Dang,

Sun,

et al. 2023

Electronics

View full text Add to dashboard Cite

In order to improve the UAV prevention and control capability in key areas and improve the rapid identification and trajectory prediction accuracy of the ground detection system in anti-UAV early warnings, an improved LSTM trajectory prediction network CA-LSTM (CNN-Attention-LSTM) based on attention enhancement and convolution fusion structure is proposed. Firstly, the native Yolov5 network is improved to enhance its detection ability for small targets of infrared UAVs, and the trajectory of UAVs in image space is constructed. Secondly, the LSTM network and convolutional neural network are integrated to improve the expression ability of the deep features of UAV trajectory data, and at the same time, the attention structure is designed to more comprehensively obtain time series context information, improve the influence on important time series features, and realize coarse–fine-grained feature fusion. Finally, tests were carried out on a homemade UAV infrared detection dataset. The experimental results show that the algorithm proposed in this paper can quickly and accurately identify infrared UAV targets and can achieve more accurate predictions of UAV flight trajectories, which are reduced by 9.43% and 23.81% in terms of MAPE and MSE indicators compared with the native LSTM network (the smaller the values of these evaluation indexes, the better the prediction results).

show abstract

Section: Uav Target Recognition Algorithmmentioning

confidence: 99%

CA-LSTM: An Improved LSTM Trajectory Prediction Method Based on Infrared UAV Target Detection

Dang,

Sun,

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Circular motion tracking paths enable high-resolution scenes without noise or blurry effects. This is a key factor in restoring 3D models saturated with realistic details from scenes collected by this powerful acquisition device [8]. The technical development of drone camera sensors has provided new capabilities for capturing aerial information in the form of high-resolution images from different destinations, angles, and heights [9].…”

Section: Introductionmentioning

confidence: 99%

PIXGAN-Drone: 3D Avatar of Human Body Reconstruction From Multi-View 2D Images

Salim Rasheed,

Jabberi,

Hamdani

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Research on training Generative Adversarial Networks (GANs) to create 3D human body avatars from 2D datasets is underway. Research done in this field shows promise and has paved the way for significant advancements in a variety of applications, including virtual reality, sports analysis, cinematography, surveillance, and cinematography. By avoiding obstacles and producing high-resolution, rich-information multi-view (RGB) images, drone active tracking combined with aerial photography sensors can eliminate occlusions and enable 3D avatar body reconstruction. Due to several issues, such as restricted perspective coverage, obvious occlusions, and texture disappearance, 3D avatar reconstruction techniques encounter training failures that cause distortions and feature loss in 3D reconstructed models. The new endto-end trainable deep neural network methodology PIXGAN-Drone is presented in this paper for the photorealistic 3D avatar of the human body reconstruction from multi-view images. It is based on the integration of active tracking drones equipped with aerial photography sensors (stable automatic circular motion system) into the Pix2Pix GANs training framework to generate high-resolution 2D models. Conditional image-toimage translation and dynamic aerial perspectives can be used to develop realistic and accurate 3D models. This research conducted experiments on multiple datasets to demonstrate the improved performance of our method over state-of-the-art methods for various metrics (Chamfer, P2S, and CED). The results showed that our 3D reconstructed human avatars were 0.0293, 0.0271, and 0.0232 on RenderPeople, 0.0133, 0.0136, 0.0050 on People Snapshot (indoor), 0.0154, 0.0101, 0.0063 on People Snapshot (outdoor), and 0.0316, 0.0275, 0.0216 on Custom data-drone (collected dataset).INDEX TERMS 3D human avatar, 3D reconstruction, PIX2PIXGAN, body model rendering, drone active tracking.

show abstract

“…When combined or even embedded in tools such as robotic total stations, resulting in “smart station” equipment, or in tools such as Unmanned Aerial Systems (UASs) [ 2 , 5 ], this technology truly contributes to the development and evolution of far-field measurement works [ 1 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Measurements for 3D Modeling and Monitoring of Large Buildings Using Terrestrial Laser Scanning and Unmanned Aerial Systems

Nap

CHIOREAN

Cira

et al. 2023

Sensors

View full text Add to dashboard Cite

Along with the development and improvement of measuring technologies and techniques in recent times, new methods have appeared to model and monitor the behavior of land and constructions over time. The main purpose of this research was to develop a new methodology to model and monitor large buildings in a non-invasive way. The methods proposed in this research are non-destructive and can be used to monitor the behavior of buildings over time. A method of comparing point clouds obtained using terrestrial laser scanning combined with aerial photogrammetric methods was used in this study. The advantages and disadvantages of using non-destructive measurement techniques over the classic methods were also analyzed. With a building located in the University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca campus as a case study and with the help of the proposed methods, the deformations over time of the facades of that building were determined. As one of the main conclusions of this case study, it can be stated that the proposed methods are adequate to model and monitor the behavior of constructions over time, ensuring a satisfactory degree of precision and accuracy. The methodology can be successfully applied to other similar projects.

show abstract

Vision-Based Navigation Techniques for Unmanned Aerial Vehicles: Review and Challenges

Cited by 93 publications

References 116 publications

CA-LSTM: An Improved LSTM Trajectory Prediction Method Based on Infrared UAV Target Detection

CA-LSTM: An Improved LSTM Trajectory Prediction Method Based on Infrared UAV Target Detection

PIXGAN-Drone: 3D Avatar of Human Body Reconstruction From Multi-View 2D Images

Non-Destructive Measurements for 3D Modeling and Monitoring of Large Buildings Using Terrestrial Laser Scanning and Unmanned Aerial Systems

Contact Info

Product

Resources

About