Zobrazeno 1 - 10
of 1 635
pro vyhledávání: '"small object detection"'
Publikováno v:
Gong-kuang zidonghua, Vol 50, Iss 8, Pp 105-111 (2024)
In order to effectively detect and recognize whether the personnel on the mining face in coal mines are wearing safety protection devices, a small object detection method based on improved YOLOv8n is proposed. It is applied in situations such as poor
Externí odkaz:
https://doaj.org/article/0fc8dc4e25a540eeb82bf341e637c256
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 17, Iss 1, Pp 1-9 (2024)
Abstract With the rapid development of unmanned aerial vehicle (UAV) technology, there is an urgent need for high-performance aerial object detection algorithms that are tailored for deployment on drones with limited computing capabilities. This pape
Externí odkaz:
https://doaj.org/article/28796551c0894fa28714f0847fb4dac9
Publikováno v:
Plant Methods, Vol 20, Iss 1, Pp 1-16 (2024)
Abstract Fungal diseases are the main factors affecting the quality and production of vegetables. Rapid and accurate detection of pathogenic spores is of great practical significance for early prediction and prevention of diseases. However, there are
Externí odkaz:
https://doaj.org/article/234d21fb2ded4bd2aa84a5b9e31118a8
Autor:
HE Zhiqian, CAO Lijie
Publikováno v:
智能科学与技术学报, Vol 6, Pp 262-271 (2024)
An improved UAVAI-YOLO model was proposed to address the problem of poor target detection in UAV aerial images. Firstly, in order to obtain richer semantic information for the model, the original convolution of the C2f module of the original backbone
Externí odkaz:
https://doaj.org/article/5cf13075e6544c5f845c813529b31db3
Publikováno v:
Gong-kuang zidonghua, Vol 50, Iss 6, Pp 61-69 (2024)
The existing deep learning based foreign object detection models for conveyor belts are relatively large and difficult to deploy on edge devices. There are errors and omissions in detecting foreign objects of different sizes and small objects. In ord
Externí odkaz:
https://doaj.org/article/82c3428a8b004e7e83b6a3f04da7c71e
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 36, Iss 9, Pp 102223- (2024)
Vehicle detection in congested urban scenes is essential for traffic control and safety management. However, the dense arrangement and occlusion of multi-scale vehicles in such environments present considerable challenges for detection systems. To ta
Externí odkaz:
https://doaj.org/article/287db4db95a1474589ed6600ef0a06d0
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 4, Pp 5459-5473 (2024)
Abstract Object detection plays a vital role in remote sensing applications. Although object detection has achieved proud results in natural images, these methods are difficult to be directly applied to remote sensing images. Remote sensing images of
Externí odkaz:
https://doaj.org/article/cac493ca70fb4bd29d25dbc2089b3562
Publikováno v:
Radioengineering, Vol 33, Iss 1, Pp 12-23 (2024)
With the advancement of various aerial platforms, there is an increasing abundance of aerial images captured in various environments. However, the detection of densely packed small objects within complex backgrounds remains a challenge. To address th
Externí odkaz:
https://doaj.org/article/0cbe49d940d749b78bf604f6ff12ddb6
Publikováno v:
Gong-kuang zidonghua, Vol 50, Iss 3, Pp 35-41 (2024)
Although current small object detection methods have improved the detection performance, they are mostly objected at conventional scenarios. In harsh underground environments in coal mines, there are difficulties in extracting small object feature in
Externí odkaz:
https://doaj.org/article/6d690cef805d4d6dbfc587c7f4fc21e5
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
Object detection on drone-view images is vital for applications like intelligent transportation, abnormal behavior detection, and urban surveillance. However, the diverse perspectives and altitudes from which Unmanned Aerial Vehicle (UAV) capture sce
Externí odkaz:
https://doaj.org/article/7a506203bf93413e9e7f2797a103a717