Zobrazeno 1 - 10
of 332
pro vyhledávání: '"underwater target detection"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-22 (2024)
Abstract In response to the challenges of target misidentification, missed detection, and other issues arising from severe light attenuation, low visibility, and complex environments in current underwater target detection, we propose a lightweight lo
Externí odkaz:
https://doaj.org/article/e138211b33a74249827aa81880eb2c90
Publikováno v:
水下无人系统学报, Vol 32, Iss 5, Pp 846-854 (2024)
Underwater target detection is often more susceptible to domain shift and reduced detection accuracy. In response to this phenomenon, this article proposed a domain-adaptive underwater target detection method based on graph-induced prototype alignmen
Externí odkaz:
https://doaj.org/article/ff80c1935cc447408f7a0f9096c82a71
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)
Abstract The underwater target detection is the most important part of monitoring for environment, ocean, and other fields. However, the detection accuracy is greatly decreased by the poor image quality resulted from the complex underwater environmen
Externí odkaz:
https://doaj.org/article/f4c18418fd0a42a4a386f166c44fb6d7
Publikováno v:
Zhihui kongzhi yu fangzhen, Vol 46, Iss 3, Pp 30-35 (2024)
Due to its excellent concealment and difficulty in deployment and removal, mines have become a recognized challenge for the world navy in MCM operation. Airborne blue-green Lidar technology is one of the new technologies in ocean exploration. Due to
Externí odkaz:
https://doaj.org/article/fc8badfeeda643eeaa10b201d280371e
Publikováno v:
Frontiers in Marine Science, Vol 11 (2024)
The application of computer vision in fish identification facilitates researchers and managers to better comprehend and safeguard the aquatic ecological environment. Numerous researchers have harnessed deep learning methodologies for studying fish sp
Externí odkaz:
https://doaj.org/article/f0b58439a73f4c448257931bc414936d
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 17263-17277 (2024)
Underwater hyperspectral imaging is crucial for various marine applications, with underwater hyperspectral target detection (HTD) holding significant importance. However, existing research on underwater HTD is limited, as most methods fail to adequat
Externí odkaz:
https://doaj.org/article/be79e08f0a0b4275ad3497f933938aa2
Publikováno v:
IEEE Access, Vol 12, Pp 133937-133951 (2024)
In underwater target detection tasks, challenges such as image blurring, complex backgrounds, and aggregation of small targets lead to problems such as difficulty in model feature extraction, target leakage, and false detection. In order to improve t
Externí odkaz:
https://doaj.org/article/0e1568a38a3e46f3a7f0d24a56b0233b
Autor:
Anwar Khan, Mostafa M. Fouda, Dinh-Thuan Do, Abdulaziz Almaleh, Abdullah M. Alqahtani, Atiq Ur Rahman
Publikováno v:
IEEE Access, Vol 12, Pp 12618-12635 (2024)
This paper provides a study of the latest target (object) detection algorithms for underwater wireless sensor networks (UWSNs). To ensure selection of the latest and state-of-the-art algorithms, only algorithms developed in the last seven years are t
Externí odkaz:
https://doaj.org/article/5eeaa1813ea24a3e99363376a8f8f8fb
Publikováno v:
Remote Sensing, Vol 16, Iss 22, p 4134 (2024)
The acquisition of side-scan sonar (SSS) images is complex, expensive, and time-consuming, making it difficult and sometimes impossible to obtain rich image data. Therefore, we propose a novel image generation algorithm to solve the problem of insuff
Externí odkaz:
https://doaj.org/article/e0c7a09eb16043a180b0caca3ae8caf7
Publikováno v:
Sensors, Vol 24, Iss 15, p 5060 (2024)
Side-scan sonar is a principal technique for subsea target detection, where the quantity of sonar images of seabed targets significantly influences the accuracy of intelligent target recognition. To expand the number of representative side-scan sonar
Externí odkaz:
https://doaj.org/article/f99abb3d9ba849549e3795754bae49d0