The Fuzzy-Based Visual Intelligent Guidance System of an Autonomous Underwater Vehicle: Realization of Identifying and Tracking Underwater Target Objects

Lin, Yong; Yu, Chao-Ming; Huang, Joyce Yi-Tzu; Wu, Chia-Yu

doi:10.1007/s40815-022-01327-7

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Yang et al (Yang, 2023) proposed a multi receiver synthetic aperture sonar (SAS) that generates high resolution by coherently stacking continuous echo signals. Lin et al (2022b) proposed a system tailored for recognizing and tracking underwater target objects. Li et al (Li et al, 2021) proposed an underwater image enhancement network guided by mid projection, which utilizes multi-color space embedding and physical model learning methods to effectively improve visual quality.…”

Section: Image Object Recognitionmentioning

confidence: 99%

Multi-scale aware turbulence network for underwater object recognition

Zhou,

Cai,

Jia

et al. 2024

Front. Mar. Sci.

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Underwater imagery is subject to distortion, and the presence of turbulence in the fluid medium poses difficulties in accurately discerning objects. To tackle these challenges pertaining to feature extraction, this research paper presents a novel approach called the multi-scale aware turbulence network (MATNet) method for underwater object identification. More specifically, the paper introduces a module known as the multi-scale feature extraction pyramid network module, which incorporates dense linking strategies and position learning strategies to preprocess object contour features and texture features. This module facilitates the efficient extraction of multi-scale features, thereby enhancing the effectiveness of the identification process. Following that, the extracted features undergo refinement through comparison with positive and negative samples. Ultimately, the study introduces multi-scale object recognition techniques and establishes a multi-scale object recognition network for the precise identification of underwater objects, utilizing the enhanced multi-scale features. This process entails rectifying the distorted image and subsequently recognizing the rectified object. Extensive experiments conducted on an underwater distorted image enhancement dataset demonstrate that the proposed method surpasses state-of-the-art approaches in both qualitative and quantitative evaluations.

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Section: Image Object Recognitionmentioning

confidence: 99%