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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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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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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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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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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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Zubair Saeed, Muhammad Haroon Yousaf, Rehan Ahmed, Sergio A. Velastin and Serestina Viriri
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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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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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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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