Detection of maize tassels for UAV remote sensing image with an improved YOLOX Model DOI Creative Commons

Chaoyu Song,

Fan Zhang,

Jian-sheng LI

et al.

Journal of Integrative Agriculture, Journal Year: 2022, Volume and Issue: 22(6), P. 1671 - 1683

Published: Sept. 24, 2022

Maize tassels detection is essential for future agronomic management in maize planting and breeding, with application yield estimation, growth monitoring, intelligent picking, disease detection, etc. Nevertheless, some problems are gradually becoming more prominent it. shown the field widespread occlusions differ size morphological color of different stages. Aiming at these issues, this study proposes SEYOLOX-tiny model that detects accurately robustness. Firstly, data acquisition method better balanced image quality efficiency obtained images from periods to enrich our dataset by unmanned aerial vehicle (UAV). Moreover, robust network extends YOLOX embedding an attention mechanism realize extraction critical features suppressing noise caused adverse factors (occlusions, overlaps, etc.), which could be suitable operating a complex natural environment. Experimental results verify current work hypothesis show mean average precision ([email protected]) was 95.0%. The [email protected], [email protected], [email protected](area=small), [email protected](area=medium) increased 1.5, 1.8, 5.3, 1.7%, respectively than original model, proposed can meet robustness vision system detection.

Language: Английский

Comparing YOLOv3, YOLOv4 and YOLOv5 for Autonomous Landing Spot Detection in Faulty UAVs DOI Creative Commons
Upesh Nepal, Hossein Eslamiat

Sensors, Journal Year: 2022, Volume and Issue: 22(2), P. 464 - 464

Published: Jan. 8, 2022

In-flight system failure is one of the major safety concerns in operation unmanned aerial vehicles (UAVs) urban environments. To address this concern, a framework consisting following three main tasks can be utilized: (1) Monitoring health UAV and detecting failures, (2) Finding potential safe landing spots case critical detected step 1, (3) Steering to spot found 2. In paper, we specifically look at second task, where investigate feasibility utilizing object detection methods suffers an in-flight failure. Particularly, different versions YOLO objection method compare their performances for specific application location that has suffered We performance YOLOv3, YOLOv4, YOLOv5l while training them by large image dataset called DOTA Personal Computer (PC) also Companion (CC). plan use chosen algorithm on CC attached UAV, PC used verify trends see between algorithms CC. confirm these effective emergency report accuracy speed application. Our investigation shows outperforms YOLOv4 YOLOv3 terms maintaining slightly slower inference speed.

Language: Английский

Citations

363

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey DOI Creative Commons
Mohammad Mahdi Azari, Sourabh Solanki, Symeon Chatzinotas

et al.

IEEE Communications Surveys & Tutorials, Journal Year: 2022, Volume and Issue: 24(4), P. 2633 - 2672

Published: Jan. 1, 2022

Non-terrestrial networks (NTNs) traditionally have certain limited applications. However, the recent technological advancements and manufacturing cost reduction opened up myriad applications of NTNs for 5G beyond networks, especially when integrated into terrestrial (TNs). This article comprehensively surveys evolution highlighting their relevance to essentially, how it will play a pivotal role in development 6G ecosystem. We discuss important features integration TNs synergies by delving new range services use cases, various architectures, enablers, higher layer aspects pertinent integration. Moreover, we review corresponding challenges arising from technical peculiarities approaches being adopted develop efficient ground-air-space (GAS) networks. Our survey further includes major progress outcomes academic research as well industrial efforts representing main trends, field trials, prototyping towards

Language: Английский

Citations

273

UAV in the advent of the twenties: Where we stand and what is next DOI Creative Commons
Francesco Nex,

Costas Armenakis,

Michael Cramer

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2022, Volume and Issue: 184, P. 215 - 242

Published: Jan. 17, 2022

The use of Unmanned Aerial Vehicles (UAVs) has surged in the last two decades, making them popular instruments for a wide range applications, and leading to remarkable number scientific contributions geoscience, remote sensing engineering. However, development best practices high quality UAV mapping are often overlooked representing drawback their wider adoption. solutions then require an inter-disciplinary research, integrating different expertise combining several hardware software components on same platform. Despite peer-reviewed papers UAVs, little attention been given interaction between research topics from domains (such as robotics computer vision) that impact sensing. aim this paper is (i) review UAVs applications (ii) report current trends - including adjacent discuss future photogrammetry Hardware developments, navigation acquisition strategies, emerging data processing innovative considered analysis. As heterogeneity debated large, organized according very specific questions most relevant by authors.

Language: Английский

Citations

195

Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey DOI
Xin Wu, Wei Li, Danfeng Hong

et al.

IEEE Geoscience and Remote Sensing Magazine, Journal Year: 2021, Volume and Issue: 10(1), P. 91 - 124

Published: Nov. 4, 2021

Owing to effective and flexible data acquisition, unmanned aerial vehicle (UAV) has recently become a hotspot across the fields of computer vision (CV) remote sensing (RS). Inspired by recent success deep learning (DL), many advanced object detection tracking approaches have been widely applied various UAV-related tasks, such as environmental monitoring, precision agriculture, traffic management. This paper provides comprehensive survey on research progress prospects DL-based UAV methods. More specifically, we first outline challenges, statistics existing methods, provide solutions from perspectives models in three topics: image, video, video. Open datasets related UAV-dominated are exhausted, four benchmark employed for performance evaluation using some state-of-the-art Finally, considerations future work discussed summarized. It is expected that this can facilitate those researchers who come field with an overview along thoughts their further developments.

Language: Английский

Citations

189

A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications DOI Creative Commons
Zhengxin Zhang, Lixue Zhu

Drones, Journal Year: 2023, Volume and Issue: 7(6), P. 398 - 398

Published: June 15, 2023

In recent years, UAV remote sensing has gradually attracted the attention of scientific researchers and industry, due to its broad application prospects. It been widely used in agriculture, forestry, mining, other industries. UAVs can be flexibly equipped with various sensors, such as optical, infrared, LIDAR, become an essential observation platform. Based on sensing, obtain many high-resolution images, each pixel being a centimeter or millimeter. The purpose this paper is investigate current applications well aircraft platforms, data types, elements category; processing methods, etc.; study advantages technology, limitations, promising directions that still lack applications. By reviewing papers published field we found research classified into four categories according field: (1) Precision including crop disease observation, yield estimation, environmental observation; (2) Forestry forest identification, disaster (3) Remote power systems; (4) Artificial facilities natural environment. We image (RGB, multi-spectral, hyper-spectral) mainly neural network methods; monitoring, multi-spectral are most studied type data; for LIDAR data, end-to-end method; review examines development process certain fields implementation some predictions made about possible future directions.

Language: Английский

Citations

145

A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs) DOI Creative Commons
Khaled Telli, Okba Kraa, Yassine Himeur

et al.

Systems, Journal Year: 2023, Volume and Issue: 11(8), P. 400 - 400

Published: Aug. 2, 2023

The growing interest in unmanned aerial vehicles (UAVs) from both the scientific and industrial sectors has attracted a wave of new researchers substantial investments this expansive field. However, due to wide range topics subdomains within UAV research, newcomers may find themselves overwhelmed by numerous options available. It is therefore crucial for those involved research recognize its interdisciplinary nature connections with other disciplines. This paper presents comprehensive overview field, highlighting recent trends advancements. Drawing on literature reviews surveys, review begins classifying UAVs based their flight characteristics. then provides an current UAVs, utilizing data Scopus database quantify number documents associated each direction interconnections. also explores potential areas further development including communication, artificial intelligence, remote sensing, miniaturization, swarming cooperative control, transformability. Additionally, it discusses aircraft commonly used control techniques, appropriate algorithms research. Furthermore, addresses general hardware software architecture applications, key issues them. open source projects By presenting view aims enhance our understanding rapidly evolving highly area

Language: Английский

Citations

130

The Segment Anything Model (SAM) for remote sensing applications: From zero to one shot DOI Creative Commons
Lucas Prado Osco, Qiusheng Wu, Eduardo Lopes de Lemos

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2023, Volume and Issue: 124, P. 103540 - 103540

Published: Nov. 1, 2023

Segmentation is an essential step for remote sensing image processing. This study aims to advance the application of Segment Anything Model (SAM), innovative segmentation model by Meta AI, in field analysis. SAM known its exceptional generalization capabilities and zero-shot learning, making it a promising approach processing aerial orbital images from diverse geographical contexts. Our exploration involved testing across multi-scale datasets using various input prompts, such as bounding boxes, individual points, text descriptors. To enhance model's performance, we implemented novel automated technique that combines text-prompt-derived general example with one-shot training. adjustment resulted improvement accuracy, underscoring SAM's potential deployment imagery reducing need manual annotation. Despite limitations, encountered lower spatial resolution images, exhibits adaptability data We recommend future research proficiency through integration supplementary fine-tuning techniques other networks. Furthermore, provide open-source code our modifications on online repositories, encouraging further broader adaptations domain.

Language: Английский

Citations

126

AI meets UAVs: A survey on AI empowered UAV perception systems for precision agriculture DOI
Jinya Su, Xiaoyong Zhu, Shihua Li

et al.

Neurocomputing, Journal Year: 2022, Volume and Issue: 518, P. 242 - 270

Published: Nov. 11, 2022

Language: Английский

Citations

123

Semantic segmentation using Vision Transformers: A survey DOI

Hans Thisanke,

Chamli Deshan,

Kavindu Chamith

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 126, P. 106669 - 106669

Published: July 29, 2023

Language: Английский

Citations

91

Forecasting vegetation indices from spatio-temporal remotely sensed data using deep learning-based approaches: A systematic literature review DOI
Aya Ferchichi, Ali Ben Abbes, Vincent Barra

et al.

Ecological Informatics, Journal Year: 2022, Volume and Issue: 68, P. 101552 - 101552

Published: Jan. 12, 2022

Language: Английский

Citations

90