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Ziwei Tian, Jie Huang, Yang Yang and Weiying Nie
Aerial remote sensing image object detection, based on deep learning, is of great significance in geological resource exploration, urban traffic management, and military strategic information. To improve intractable problems in aerial remote sensing imag...
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Atakan Körez, Necaattin Barisçi, Aydin Çetin and Uçman Ergün
The detection of objects in very high-resolution (VHR) remote sensing images has become increasingly popular with the enhancement of remote sensing technologies. High-resolution images from aircrafts or satellites contain highly detailed and mixed backgr...
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Yanhua Shao, Xingping Zhang, Hongyu Chu, Xiaoqiang Zhang, Duo Zhang and Yunbo Rao
Aerial object detection acts a pivotal role in searching and tracking applications. However, the large model, limited memory, and computing power of embedded devices restrict aerial pedestrian detection algorithms? deployment on the UAV (unmanned aerial ...
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Xinmin Li, Yingkun Wei, Jiahui Li, Wenwen Duan, Xiaoqiang Zhang and Yi Huang
Object detection in unmanned aerial vehicle (UAV) images has become a popular research topic in recent years. However, UAV images are captured from high altitudes with a large proportion of small objects and dense object regions, posing a significant cha...
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Ahad Alotaibi, Chris Chatwin and Phil Birch
In aerial surveillance systems, achieving optimal object detection precision is of paramount importance for effective monitoring and reconnaissance. This article presents a novel approach to enhance object detection accuracy through the integration of De...
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Linhua Zhang, Ning Xiong, Xinghao Pan, Xiaodong Yue, Peng Wu and Caiping Guo
In unmanned aerial vehicle photographs, object detection algorithms encounter challenges in enhancing both speed and accuracy for objects of different sizes, primarily due to complex backgrounds and small objects. This study introduces the PDWT-YOLO algo...
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Chuanyun Wang, Linlin Meng, Qian Gao, Jingjing Wang, Tian Wang, Xiaona Liu, Furui Du, Linlin Wang and Ershen Wang
Aiming at the problems of low detection accuracy and large computing resource consumption of existing Unmanned Aerial Vehicle (UAV) detection algorithms for anti-UAV, this paper proposes a lightweight UAV swarm detection method based on You Only Look Onc...
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Zubair Saeed, Muhammad Haroon Yousaf, Rehan Ahmed, Sergio A. Velastin and Serestina Viriri
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Liming Zhou, Xiaohan Rao, Yahui Li, Xianyu Zuo, Baojun Qiao and Yinghao Lin
In recent years, significant progress has been obtained in object detection using Convolutional Neural Networks (CNNs). However, owing to the particularity of Remote Sensing Images (RSIs), common object detection methods are not well suited for RSIs. Aim...
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Ziyang Tang, Xiang Liu, Hanlin Chen, Joseph Hupy and Baijian Yang
Unmanned Aerial Systems, hereafter referred to as UAS, are of great use in hazard events such as wildfire due to their ability to provide high-resolution video imagery over areas deemed too dangerous for manned aircraft and ground crews. This aerial pers...
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Dunja Bo?ic-?tulic,Stanko Kru?ic,Sven Gotovac,Vladan Papic
Pág. 82 - 90
In this paper, a novel approach for an automatic object detection and localisation on aerial images is proposed. Proposed model does not use ground control points (GCPs) and consists of three major phases. In the first phase, optimal flight route is plan...
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Yuzhuang Wan, Yi Zhong, Yan Huang, Yi Han, Yongqiang Cui, Qi Yang, Zhuo Li, Zhenhui Yuan and Qing Li
Due to the rapid development of deep learning, the performance of object detection has greatly improved. However, object detection in high-resolution Unmanned Aerial Vehicles images remains a challenging problem for three main reasons: (1) the objects in...
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Hy Nguyen, Srikanth Thudumu, Hung Du, Kon Mouzakis and Rajesh Vasa
Several approaches have applied Deep Reinforcement Learning (DRL) to Unmanned Aerial Vehicles (UAVs) to do autonomous object tracking. These methods, however, are resource intensive and require prior knowledge of the environment, making them difficult to...
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Ming-An Chung, Tze-Hsun Wang and Chia-Wei Lin
Environmental, social, and governance issues have gained significant prominence recently, particularly with a growing emphasis on environmental protection. In the realm of heightened environmental concerns, unmanned aerial vehicles have emerged as pivota...
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Mirela Kundid Vasic and Vladan Papic
Recent results in person detection using deep learning methods applied to aerial images gathered by Unmanned Aerial Vehicles (UAVs) have demonstrated the applicability of this approach in scenarios such as Search and Rescue (SAR) operations. In this pape...
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Marios Mamalis, Evangelos Kalampokis, Ilias Kalfas and Konstantinos Tarabanis
The verticillium fungus has become a widespread threat to olive fields around the world in recent years. The accurate and early detection of the disease at scale could support solving the problem. In this paper, we use the YOLO version 5 model to detect ...
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Wen-Chieh Chen, Chun-Liang Lin, Yang-Yi Chen and Hsin-Hsu Cheng
Unmanned aerial vehicles (UAVs) are becoming popular in various applications. However, there are still challenging issues to be tackled, such as effective obstacle avoidance, target identification within a crowd, and specific target tracking. This paper ...
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?imon Grác, Peter Beno, Franti?ek Duchon, Martin Dekan and Michal Tölgyessy
The objective of this article is to propose and verify a reliable detection mechanism of multi-rotor unmanned aerial vehicles (UAVs). Such a task needs to be solved in many areas such as in the protection of vulnerable buildings or in the protection of p...
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Salvatore Barba, Maurizio Barbarella, Alessandro Di Benedetto, Margherita Fiani, Lucas Gujski and Marco Limongiello
The unmanned aerial vehicle (UAV) photogrammetric survey of an archaeological site has proved itself to be particularly efficient. In order to obtain highly accurate and reliable results, it is necessary to design carefully the flight plan and the geo-re...
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Yu-Hsien Liao and Jih-Gau Juang
Plastic trash can be found anywhere, around the marina, beaches, and coastal areas in recent times. This study proposes a trash dataset called HAIDA and a trash detector that uses a YOLOv4-based object detection algorithm to monitor coastal trash polluti...
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