Underwater target recognition methods based on the framework of deep learning: A survey

Teng, Bowen; Zhao, Huijing

doi:10.1177/1729881420976307

Cited by 43 publications

(31 citation statements)

References 64 publications

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“…The second survey reviews underwater target recognition methods based on deep learning [104]. They focus on the discussion of dangerous underwater target recognition, in which methods are divided into three categories: target recognition based on shape feature, unsupervised recognition technology, and deep learning method based on CNNs.…”

Section: Comparisons With Previous Reviewsmentioning

confidence: 99%

“…The pros and cons of various methods are discussed. The authors of [104] also discussed the challenges in few-shot target recognition and target recognition under environmental interference conditions. Finally, they compared different algorithms on UDD dataset [106], and concluded that poor universality of algorithms is always a big problem in this field.…”

Section: Comparisons With Previous Reviewsmentioning

confidence: 99%

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Research Challenges, Recent Advances and Benchmark Datasets in Deep-Learning-Based Underwater Marine Object Detection: A Review

Er¹,

Chen²

2022

Preprint

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Underwater marine object detection, as one of the most fundamental techniques in marine engineering, had been shown to exhibit tremendous potential for exploring the oceans in recent years. It has been applied widely in monitoring of underwater ecosystems, exploration of natural resources, management of commercial fishery, and so on. However, due to the complexity of the underwater environment, characteristics of marine objects, and limitations from exploration equipment, the detection performance in terms of speed, accuracy and robustness could be degraded dramatically using conventional approaches. Deep learning algorithms have found significant impact in many fields of engineering and science including marine engineering. In this context, We offer an overview of deep-learning-based underwater marine object detection techniques. We organize the present research challenges into three areas to assist a thorough knowledge of the subject matter, namely image quality degradation, small object detection, and poor generalization. We evaluate recent advances and emphasize the advantages and risks of existing solutions for each category of extant challenges. In addition, we examine the most extensively used benchmark datasets for underwater marine object detection in detail and critically. Comparisons between previous reviews and prospective advancements in the subject field, notably Artificial-Intelligence-based approaches, are also presented.

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Section: Comparisons With Previous Reviewsmentioning

confidence: 99%

Section: Comparisons With Previous Reviewsmentioning

confidence: 99%

Research Challenges, Recent Advances and Benchmark Datasets in Deep-Learning-Based Underwater Marine Object Detection: A Review

Er¹,

Chen²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The second survey reviews underwater target recognition methods based on deep learning [169]. They focus on the discussion of dangerous underwater target recognition, such as mine weapons and other man-made objects, which can not only strike submarines and block maritime traffic routes but can also cause serious psychological burdens on enemy personnel.…”

Section: Comparisons With Previous Reviewsmentioning

confidence: 99%

“…They focus on the discussion of dangerous underwater target recognition, such as mine weapons and other man-made objects, which can not only strike submarines and block maritime traffic routes but can also cause serious psychological burdens on enemy personnel. In [169], dangerous underwater target recognition methods are divided into three categories: target recognition based on shape feature, unsupervised recognition technology, and deep learning method based on CNNs. The pros and cons of various methods are discussed.…”

Section: Comparisons With Previous Reviewsmentioning

confidence: 99%

Research Challenges, Recent Advances and Benchmark Datasets in Deep-Learning-Based Underwater Marine Object Detection: A Review

Er¹,

Chen²

2022

Preprint

View full text Add to dashboard Cite

Underwater marine object detection, as one of the most fundamental techniques in marine engineering, has been shown to exhibit significant potential for exploring underwater environment in recent years. It has been applied widely in the monitoring of underwater ecosystems, exploration of natural resources, management of commercial fishery management, and other areas. However, due to the complexity of underwater environment, characteristics of marine object, and limitation from exploration equipment, the detection performance revolving around speed, accuracy and robustness could be degraded dramatically. In this context, we present a survey of deep-learning-based underwater marine object detection. To facilitate comprehensive understanding of the subject matter, we categorize the existing research challenges of underwater object detection into image quality degradation, small object detection, poor generalization and real-time detection. Corresponding to each category of the existing challenges, we review recent advances and highlight the pros and cons of existing techniques. Furthermore, we give a detailed and critical review of the most widely used benchmark datasets for underwater marine object detection. Comparisons with existing reviews and future trends of the subject matter, particularly AI-based techniques, are also presented.

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“…The rapid development of underwater observation technology provides underwater optical vision with very broad application prospects. As a typical application of underwater optical vision, underwater visual target detection plays an increasingly important role in underwater security [1][2][3][4], marine exploration [5,6], fish farming [7] and marine ecology [8,9]. Therefore, the achievement of underwater autonomous operation through visual target detection completion by use of underwater optical images has become a research hotspot in the field of computer vision [1].…”

Section: Introductionmentioning

confidence: 99%

A Marine Organism Detection Framework Based on the Joint Optimization of Image Enhancement and Object Detection

Zhang

Fang

Pan

et al. 2021

Sensors

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Underwater vision-based detection plays an increasingly important role in underwater security, ocean exploration and other fields. Due to the absorption and scattering effects of water on light, as well as the movement of the carrier, underwater images generally have problems such as noise pollution, color cast and motion blur, which seriously affect the performance of underwater vision-based detection. To address these problems, this study proposes an end-to-end marine organism detection framework that can jointly optimize the image enhancement and object detection. The framework uses a two-stage detection network with dynamic intersection over union (IoU) threshold as the backbone and adds an underwater image enhancement module (UIEM) composed of denoising, color correction and deblurring sub-modules to greatly improve the framework’s ability to deal with severely degraded underwater images. Meanwhile, a self-built dataset is introduced to pre-train the UIEM, so that the training of the entire framework can be performed end-to-end. The experimental results show that compared with the existing end-to-end models applied to marine organism detection, the detection precision of the proposed framework can improve by at least 6%, and the detection speed has not been significantly reduced, so that it can complete the high-precision real-time detection of marine organisms.

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Underwater target recognition methods based on the framework of deep learning: A survey

Cited by 43 publications

References 64 publications

Research Challenges, Recent Advances and Benchmark Datasets in Deep-Learning-Based Underwater Marine Object Detection: A Review

Research Challenges, Recent Advances and Benchmark Datasets in Deep-Learning-Based Underwater Marine Object Detection: A Review

Research Challenges, Recent Advances and Benchmark Datasets in Deep-Learning-Based Underwater Marine Object Detection: A Review

A Marine Organism Detection Framework Based on the Joint Optimization of Image Enhancement and Object Detection

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