Zobrazeno 1 - 10
of 1 454
pro vyhledávání: '"multi -scale feature fusion"'
Publikováno v:
Journal of Multidisciplinary Healthcare, Vol Volume 17, Pp 5675-5693 (2024)
Xinzheng Wang, Cuisi Ou, Zhigang Hu, Aoru Ge, Yipei Wang, Kaiwen Cao School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang City, Henan Province, People’s Republic of ChinaCorrespondence: Zhigang Hu, Schoo
Externí odkaz:
https://doaj.org/article/3ada3605819943f4aa363c52edab6629
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract The exponential growth in the number of registered trademarks, coupled with the escalating incidents of trademark infringement, has made the automatic detection of such infractions a crucial area of study in the domain of market regulation.
Externí odkaz:
https://doaj.org/article/d24e6f21bf9444bdae42475492e310ca
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)
Abstract Accurate consumption forecasting is of great importance to grasp the energy consumption habits of consumers and promote the stable and efficient operation of integrated energy system (IES). To this end, this paper proposes an interactive mul
Externí odkaz:
https://doaj.org/article/f65835b2d48e4b309bd183c4808f147c
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
Side-scan sonar image target detection is of great significance in seabed resource exploration and other fields. However, affected by the complex underwater environment, side-scan sonar images have the problems of few target samples and large differe
Externí odkaz:
https://doaj.org/article/bb9490ade1d54df6b71b1a856c0eac6f
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
ABSTRACTDeep learning (DL) models have been widely used for remote sensing-based landslide mapping due to their impressive capabilities for automatic information extraction. However, the large volumes of parameters and calculations have compromised t
Externí odkaz:
https://doaj.org/article/ac9a2c0cf66544e68e90d67298682c16
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:
International Journal of Applied Earth Observations and Geoinformation, Vol 134, Iss , Pp 104200- (2024)
Recently, Autoencoders (AEs) have demonstrated remarkable performance in the field of hyperspectral anomaly detection, owing to their powerful capability in handling high-dimensional data. However, they often overlook the inherent global distribution
Externí odkaz:
https://doaj.org/article/17f9642dfb3a45859d3d6c08a5be8811
Publikováno v:
Journal of Hydroinformatics, Vol 26, Iss 5, Pp 1234-1250 (2024)
To solve the problems of localization and identification of fish in the complex fishway environment, improving the accuracy of fish detection, this paper proposes an object detection algorithm YOLORG, and a fishway fish detection dataset (FFDD). The
Externí odkaz:
https://doaj.org/article/75d00323e4ac4e21868a31eb776688ce
Publikováno v:
Heliyon, Vol 10, Iss 18, Pp e38088- (2024)
Accurate identification of Mycobacterium tuberculosis (M. tuberculosis) is a critical step in the diagnosis of tuberculosis. Existing object detection methods struggle with the challenges posed by the varied morphology and size of M. tuberculosis in
Externí odkaz:
https://doaj.org/article/942e5eb597884bb5b4abec17a383fb93
Publikováno v:
Heliyon, Vol 10, Iss 18, Pp e37655- (2024)
Online signature verification (OSV) is widely used in finance, law and other fields, and is one of the important research projects on biological characteristics. However, its data set has a small scale and has high requirements for generalization of
Externí odkaz:
https://doaj.org/article/8ff29a7ff643402e998421d6d358adb4