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EHDC: enhanced dilated convolution framework for underwater blurred target recognition

Published online by Cambridge University Press:  26 July 2022

Lei Cai Open the ORCID record for Lei Cai [Opens in a new window] ,
Xiaochen Qin Open the ORCID record for Xiaochen Qin [Opens in a new window]  and
Tao Xu
Lei Cai*
Affiliation:
School of Artificial Intelligence, Henan Institute of Science and Technology, Xinxiang, China
Xiaochen Qin
Affiliation:
School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China
Tao Xu
Affiliation:
School of Artificial Intelligence, Henan Institute of Science and Technology, Xinxiang, China
*
*Corresponding author. E-mail: cailei2014@126.com
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Abstract

The autonomous underwater vehicle (AUV) has a problem with feature loss when recognizing small targets underwater. At present, algorithms usually use multi-scale feature extraction to solve the problem, but this method increases the computational effort of the algorithm. In addition, low underwater light and turbid water result in incomplete information on target features. This paper proposes an enhanced dilated convolution framework (EHDC) for underwater blurred target recognition. Firstly, this paper extracts small target features through hybrid dilated convolution networks, increasing the perceptive field of the algorithm without increasing the computational power of the algorithm. Secondly, the proposed algorithm learns spatial semantic features through an adaptive correlation matrix and compensates for the missing features of the target. Finally, this paper fuses spatial semantic features and visual features for the recognition of small underwater blurred targets. Experiments show that the proposed method improves the recognition accuracy by 1.04% compared to existing methods when recognizing small underwater blurred targets.

Keywords

blurred small targethybrid dilated convolutionspatial semantic featureslow light conditionstarget recognition
Type
Research Article
Information
Robotica , Volume 41 , Issue 3 , March 2023 , pp. 900 - 911
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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