Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Xin-qing Wang"'
Publikováno v:
Defence Technology, Vol 28, Iss , Pp 74-86 (2023)
Traditional object detectors based on deep learning rely on plenty of labeled samples, which are expensive to obtain. Few-shot object detection (FSOD) attempts to solve this problem, learning detection objects from a few labeled samples, but the perf
Externí odkaz:
https://doaj.org/article/8a9b1b9107554ec5a890a1ae81a4bc5e
Publikováno v:
Defence Technology, Vol 21, Iss , Pp 103-115 (2023)
Detecting highly-overlapped objects in crowded scenes remains a challenging problem, especially for one-stage detector. In this paper, we extricate YOLOv4 from the dilemma in a crowd by fine-tuning its detection scheme, named YOLO-CS. Specifically, w
Externí odkaz:
https://doaj.org/article/00d9d79ee1dd4e1fb80a638cea9d3538
Publikováno v:
Defence Technology, Vol 17, Iss 4, Pp 1249-1261 (2021)
The multi-armored target tracking (MATT) plays a crucial role in coordinated tracking and strike. The occlusion and insertion among targets and target scale variation is the key problems in MATT. Most state-of-the-art multi-object tracking (MOT) work
Externí odkaz:
https://doaj.org/article/e33ce204d0bb44babac2e2f8fdbe1ee0
Autor:
Xiao-dong Hu, Xin-qing Wang, Fan-jie Meng, Xia Hua, Yu-ji Yan, Yu-yang Li, Jing Huang, Xun-lin Jiang
Publikováno v:
Defence Technology, Vol 16, Iss 6, Pp 1116-1129 (2020)
Object detection models based on convolutional neural networks (CNN) have achieved state-of-the-art performance by heavily rely on large-scale training samples. They are insufficient when used in specific applications, such as the detection of milita
Externí odkaz:
https://doaj.org/article/9cc6538eff3745f88b73e92680933df7
Publikováno v:
Minerals, Vol 12, Iss 6, p 698 (2022)
The experimental study on the water-holding characteristics of remolded loess was carried out, revealing the variation of water-holding characteristics with particle size composition and dry density. The results show that the air-entry value is posit
Externí odkaz:
https://doaj.org/article/2165f9a7cd1840f0b006d2c7e4c23753
Publikováno v:
Sustainable Energy & Fuels; 6/7/2024, Vol. 8 Issue 11, p2446-2450, 5p
Publikováno v:
Defence Technology.
Autor:
Yu-ji Yan, Fanjie Meng, Yu-yang Li, Xun-lin Jiang, Xia Hua, Xin-qing Wang, Jing Huang, Xiao-dong Hu
Publikováno v:
Defence Technology, Vol 16, Iss 6, Pp 1116-1129 (2020)
Object detection models based on convolutional neural networks (CNN) have achieved state-of-the-art performance by heavily rely on large-scale training samples. They are insufficient when used in specific applications, such as the detection of milita
Publikováno v:
Defence Technology, Vol 16, Iss 4, Pp 922-932 (2020)
Focused on the task of fast and accurate armored target detection in ground battlefield, a detection method based on multi-scale representation network (MS-RN) and shape-fixed Guided Anchor (SF-GA) scheme is proposed. Firstly, considering the large-s
Publikováno v:
Defence Technology, Vol 16, Iss 3, Pp 737-746 (2020)
Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning. In view of the characteristics of intrusion targets as well as inspection difficulties, an infrared target intrusion detection