An Improved Chirp Typed Blind Watermarking Algorithm Based on Wavelet and Fractional Fourier Transform

Wang, Yuying; Li, Hongmei; Jia, Jinyuan

doi:10.1109/icig.2007.38

Cited by 3 publications

(1 citation statement)

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“…In [2], the authors combined the development and status quo of target-detection algorithms and made prospects. Traditional algorithms include the scale-invariant feature transform (SIFT) algorithm [3], PCA-SIFT algorithm [4], speeded up robust feature (SURF) algorithm [5], etc. It is pointed out that PCA-SIFT and SURF algorithms simplify the matching process of SIFT, respectively, so the speed will be relatively fast, but the detection accuracy and positioning accuracy based on feature point matching will also decline.…”

Section: Introductionmentioning

confidence: 99%

Water Column Detection Method at Impact Point Based on Improved YOLOv4 Algorithm

et al. 2022

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For a long time, the water column at the impact point of a naval gun firing at the sea has mainly depended on manual detection methods for locating, which has problems such as low accuracy, subjectivity and inefficiency. In order to solve the above problems, this paper proposes a water column detection method based on an improved you-only-look-once version 4 (YOLOv4) algorithm. Firstly, the method detects the sea antenna through the Hoffman line detection method to constrain the sensitive area in the current detection image so as to improve the accuracy of water column detection; secondly, density-based spatial clustering of applications with noise (DBSCAN) + K-means clustering algorithm is used to obtain a better prior bounding box, which is input into the YOLOv4 network to improve the positioning accuracy of the water column; finally, the convolutional block attention module (CBAM) is added in the PANet structure to improve the detection accuracy of the water column. The experimental results show that the above algorithm can effectively improve the detection accuracy and positioning accuracy of the water column at the impact point.

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Section: Introductionmentioning

confidence: 99%