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

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 detect...

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Vydané v:Sustainability Ročník 14; číslo 22; s. 15329
Hlavní autori: Shi, Jiaowei, Sun, Shiyan, Shi, Zhangsong, Zheng, Chaobing, She, Bo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.11.2022
Predmet:
Accuracy
Algorithms
Clustering
Datasets
Deep learning
Machine vision
Naval gunnery
Semantics
Sustainability
Technology application
China
ISSN:2071-1050, 2071-1050
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2071-1050
2071-1050
DOI:10.3390/su142215329