Dynamic Multiscale Feature Fusion Method for Underwater Target Recognition

Cai, Lei; Li, Yuejun; Chen, Chuang; Chai, Haojie

doi:10.1155/2022/8110695

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…Ye et al (2022) introduced a fusion multi-scale attention mechanism network to address boundary ambiguity, utilizing a feature refinement compensation module to minimize inter-class disparities. Cai et al (2022a) proposed a dynamic multi-scale feature fusion method for underwater target recognition. introduced MLLE, an effective method for enhancing underwater images.…”

Section: Multi-scale Feature Extractionmentioning

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: Multi-scale Feature Extractionmentioning

confidence: 99%

Multi-scale aware turbulence network for underwater object recognition

Zhou,

Cai,

Jia

et al. 2024

Front. Mar. Sci.

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“…In [20], Gao et al used a single image to calibrate the camera through a specially designed checkerboard. Cai et al [21] proposed a dynamic multiscale feature fusion method for underwater object recognition, which learned the spatial semantic features of the object through dynamic conditional probability matrix to improve the accuracy of underwater distorted objects. Mozaffari et al [22] proposed a highquality eye-tracking reference frame to improve real-time active eye movement correction system for revisit accuracy between consecutive imaging sessions.…”

Section: Related Workmentioning

confidence: 99%

A Method of Image Restoration for Distortion of Object in Water-Air Cross-Media

Gao,

Jia,

Cai

et al. 2024

International Journal of Distributed Sensor Networks

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Uneven water-air media distribution or irregular liquid flow can cause changes in light propagation, leading to blurring and distortion of the extracted image, which presents a challenge for object recognition accuracy. To address these issues, this paper proposes a repair network to correct object image distortion in water-air cross-media. Firstly, convolutional combination performs feature extraction on water-air cross-media images, which retains the same features at the same scale and marks feature points with large differences. Then, an attention correction module for geometric lines is proposed to correct geometric lines in water-air cross-media images by comparing and sensing the marked feature points with large differences and utilizing the line similarity in positive and negative samples. Finally, the blurring artifact elimination module eliminates artifacts caused by image blurring and geometric line correction by using multiscale fusion of individual U-Net information streams. This completes the image restoration of object distortion under water-air cross-media. The proposed method is feasible and effective for restoring aberrated objects in water-air cross-media environments, with numerous experiments conducted on water-air cross-media image datasets.

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“…Due to the influence of complex sea conditions and refraction of light transmission, the images obtained by AUV and other underwater vision equipment have weak features such as feature blur, loss and distortion. Most of the existing methods based on deep network solve the problem of weak feature target recognition by image deblurring, feature co-occurrence relationship for feature correction [2], distortion correction [3, 4] and other methods. However, this kind of algorithm can easily lead to the loss of original spatial and semantic information of small targets [5, 6], and increase the relevant modules and computing volume of the algorithm [7].…”

Section: Introductionmentioning

confidence: 99%

IFE-net: improved feature enhancement network for weak feature target recognition in autonomous underwater vehicles

Cai,

Zhang,

et al. 2024

Robotica

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The recognizing underwater targets is a crucial component of autonomous underwater vehicle patrols and detection efforts. In the process of visual image recognition in real underwater environment, the spatial and semantic features of the target often appear to different degrees of loss, and the scarcity of specific types of underwater samples leads to unbalanced data on categories. This kind of problem makes the target features appear weak and seriously affects the accuracy of underwater target recognition. Traditional deep learning methods based on data and feature enhancement cannot achieve ideal recognition effect. Based on the above difficulties, this paper proposes an improved feature enhancement network for weak feature target recognition. Firstly, a multi-scale spatial and semantic feature enhancement module is constructed to extract the feature information of the extraction target accurately. Secondly, this paper solves the influence of target feature distortion on classification through multi-scale feature comparison of positive and negative samples. Finally, the Rank & Sort Loss function was used to train the depth target detection to solve the problem of recognition accuracy under highly unbalanced sample data. Experimental results show that the recognition accuracy of the proposed method is 2.28% and 3.84% higher than that of the existing algorithms in the recognition of underwater fuzzy and distorted target images, which demonstrates the effectiveness and superiority of the proposed method.

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Dynamic Multiscale Feature Fusion Method for Underwater Target Recognition

Cited by 6 publications

References 33 publications

Multi-scale aware turbulence network for underwater object recognition

Multi-scale aware turbulence network for underwater object recognition

A Method of Image Restoration for Distortion of Object in Water-Air Cross-Media

IFE-net: improved feature enhancement network for weak feature target recognition in autonomous underwater vehicles

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