Zobrazeno 1 - 10
of 101
pro vyhledávání: '"Yan, Menglong"'
Autor:
Tian, Pengju, Cheng, Peirui, Wang, Yuchao, Wang, Zhechao, Wang, Zhirui, Yan, Menglong, Yang, Xue, Sun, Xian
Multi-UAV collaborative 3D object detection can perceive and comprehend complex environments by integrating complementary information, with applications encompassing traffic monitoring, delivery services and agricultural management. However, the extr
Externí odkaz:
http://arxiv.org/abs/2406.04648
Siamese network based trackers develop rapidly in the field of visual object tracking in recent years. The majority of siamese network based trackers now in use treat each channel in the feature maps generated by the backbone network equally, making
Externí odkaz:
http://arxiv.org/abs/2303.12304
Autor:
Chen, Kaiqiang, Dong, Bo, Wang, Zhirui, Cheng, Peirui, Yan, Menglong, Sun, Xian, Weinmann, Michael, Weinmann, Martin
Publikováno v:
In ISPRS Journal of Photogrammetry and Remote Sensing September 2024 215:383-399
Autor:
Han, Yurong, Yan, Menglong, Fang, Yinzhuang, Xiong, Yuchuan, Wang, Yueyue, Chen, Yi, Lin, Liangyou, Qian, Jingwen, Mei, Tao, Wang, Xianbao
Publikováno v:
In Journal of Power Sources 30 May 2024 603
Autor:
Zhu, Fengshuai, Tao, Junyang, Yan, Menglong, Huang, Suji, Irshad, Muhammad Sultan, Mei, Tao, Lin, Liangyou, Chen, Yi, Qian, Jingwen, Wang, Xianbao
Publikováno v:
In Sustainable Materials and Technologies April 2024 39
The performance of object instance segmentation in remote sensing images has been greatly improved through the introduction of many landmark frameworks based on convolutional neural network. However, the object densely issue still affects the accurac
Externí odkaz:
http://arxiv.org/abs/1904.09823
The current advances in object detection depend on large-scale datasets to get good performance. However, there may not always be sufficient samples in many scenarios, which leads to the research on few-shot detection as well as its extreme variation
Externí odkaz:
http://arxiv.org/abs/1904.02317
Deep learning based object detection has achieved great success. However, these supervised learning methods are data-hungry and time-consuming. This restriction makes them unsuitable for limited data and urgent tasks, especially in the applications o
Externí odkaz:
http://arxiv.org/abs/1904.02302
Akademický článek
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Publikováno v:
IEEE ACCESS. 2018, 6, 50839 - 50849
Ship detection is of great importance and full of challenges in the field of remote sensing. The complexity of application scenarios, the redundancy of detection region, and the difficulty of dense ship detection are all the main obstacles that limit
Externí odkaz:
http://arxiv.org/abs/1806.04828