EHDC: enhanced dilated convolution framework for underwater blurred target recognition

Cai, Lei; Qin, Xiaochen; Xu, Tao

doi:10.1017/s0263574722001059

Cited by 4 publications

(4 citation statements)

References 37 publications

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“…Kuang [20] utilized semantic information extraction and environment matching to enhance the localization capabilities of mobile robots, enabling them to operate more efficiently in their environment. Cai [21] conducted fuzzy small target feature extraction by combining hybrid dilation convolution with multi-scale features. Liu [22] learns object features and contextual feature weights based on upsampling to fuse multi-layer feature maps to improve the detection performance of small object detection performance.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…Li et al (2022a) investigated first-person hand movement recognition for RGB-D sequences with eight classical pre-trained networks and one pre-trained network designed to extract RGB-D features. Cai et al (2022c) proposed an enhanced dilated convolution framework for underwater blurred target recognition. Yamada et al (2021) proposed a novel selfsupervised representation learning method.…”

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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“…This method proves to be effective in handling real-world motion blur. This article [35] proposed to extract small object features by hybrid expansion convolutional network. Spatial semantic features are learned by adaptive correlation matrix and fused with spatial semantic features and visual features for underwater fuzzy object recognition.…”

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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EHDC: enhanced dilated convolution framework for underwater blurred target recognition

Cited by 4 publications

References 37 publications

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

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

Multi-scale aware turbulence network for underwater object recognition

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

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