Enhancement of deep-sea floor images obtained by an underwater vehicle and its evaluation by crab recognition

Ahn, Jonghyun; Yasukawa, Shinsuke; Sonoda, Takashi; Ura, Tamaki; Ishii, Kazuo

doi:10.1007/s00773-017-0442-1

Cited by 34 publications

(14 citation statements)

References 13 publications

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“…Thus it may fail to enhance degraded underwater images under such critical conditions. Ahn et al (2017) proposed an enhancement scheme for deep-sea floor images to detect the crab, using the retinex, contrast processing, and hue adjustment techniques.…”

Section: Color Compensation Based Techniquesmentioning

confidence: 99%

Passive Visual Underwater Surveillance: A Survey

Subudhi

Kapoor

Rout

et al. 2023

Preprint

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The objective of this article is to provide a detailed review of the state-of-the-art underwater surveillance process and the new trends of the same. Underwater surveillance has recently getting lots of attention because of its potential applications including the security of the coastal border, effective fish farming, deep-sea exploration, preservation of rare aquatic animals, etc. In the case of underwater, the light that is sensed by the camera got degraded due to many underwater phenomena such as haze, low illumination, scattering, absorption, diffraction, and refraction. The actual color of the object, as well as the scene, gets degraded as the light can not travel deep underwater. In a situation, where the scene of view is not clear, it is very difficult to detect the moving objects present in the scene. Further, the identification of the object of concern and tracking of that becomes more challenging. In this survey, we categorize underwater surveillance as a combination of the three blocks: enhancement, object detection, and object tracking. We categorize all these three blocks based on the underwater complexity and motion models considered. In this article, we tried to enumerate the most detailed descriptions of each category. We also discuss the future directions of research in the area of underwater surveillance.

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Section: Color Compensation Based Techniquesmentioning

confidence: 99%

Passive Visual Underwater Surveillance: A Survey

Subudhi

Kapoor

Rout

et al. 2023

Preprint

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“…e purpose is to improve the analysis effect of the image for the given image application situation [25]. We purposefully emphasize the overall or local characteristics of the image, make the original unclear image clear or emphasize some interesting features [26], expand the difference between the features of different objects in the image, and suppress uninteresting features. e image quality is improved [27], the amount of information is more abundant [28], and the image interpretation and recognition effects are strengthened [29] to meet the needs of some special analyses.…”

Section: Multidirectional Edge Feature Enhancement Algorithmmentioning

confidence: 99%

A Marine Object Detection Algorithm Based on SSD and Feature Enhancement

Lu²,

Lu³

et al. 2020

Complexity

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Autonomous detection and fishing by underwater robots will be the main way to obtain aquatic products in the future; sea urchins are the main research object of aquatic product detection. When the classical Single-Shot MultiBox Detector (SSD) algorithm is applied to the detection of sea urchins, it also has disadvantages of being inaccurate to small targets and insensitive to the direction of the sea urchin. Based on the classic SSD algorithm, this paper proposes a feature-enhanced sea urchin detection algorithm. Firstly, according to the spiny-edge characteristics of a sea urchin, a multidirectional edge detection algorithm is proposed to enhance the feature, which is taken as the 4th channel of image and the original 3 channels of underwater image together as the input for the further deep learning. Then, in order to improve the shortcomings of SSD algorithm’s poor ability to detect small targets, resnet 50 is used as the basic framework of the network, and the idea of feature cross-level fusion is adopted to improve the feature expression ability and strengthen semantic information. The open data set provided by the National Natural Science Foundation of China underwater Robot Competition will be used as the test set and training set. Under the same training and test conditions, the AP value of the algorithm in this paper reaches 81.0%, 7.6% higher than the classic SSD algorithm, and the confidence of small target analysis is also improved. Experimental results show that the algorithm in this paper can effectively improve the accuracy of sea urchin detection.

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“…This method addresses the influence of different types of water on the enhancement results, but it often produces strange textures and is highly dependent on the training dataset. Ahn et al [ 29 ] used matched clear and unclear underwater images as a dataset for training, and the model structure of CycleGAN was adjusted to improve the effect. The multichannel CycleGAN technology proposed by Lu et al [ 30 ] addresses the influence of different types of water on the enhancement results, but it is also highly dependent on the training dataset.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Training CycleGAN to Enhance Images of Cold Seeps in the Qiongdongnan Sea

Yang

Gong

et al. 2023

Sensors

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Clear underwater images can help researchers detect cold seeps, gas hydrates, and biological resources. However, the quality of these images suffers from nonuniform lighting, a limited range of visibility, and unwanted signals. CycleGAN has been broadly studied in regard to underwater image enhancement, but it is difficult to apply the model for the further detection of Haima cold seeps in the South China Sea because the model can be difficult to train if the dataset used is not appropriate. In this article, we devise a new method of building a dataset using MSRCR and choose the best images based on the widely used UIQM scheme to build the dataset. The experimental results show that a good CycleGAN could be trained with the dataset using the proposed method. The model has good potential for applications in detecting the Haima cold seeps and can be applied to other cold seeps, such as the cold seeps in the North Sea. We conclude that the method used for building the dataset can be applied to train CycleGAN when enhancing images from cold seeps.

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Enhancement of deep-sea floor images obtained by an underwater vehicle and its evaluation by crab recognition

Cited by 34 publications

References 13 publications

Passive Visual Underwater Surveillance: A Survey

Passive Visual Underwater Surveillance: A Survey

A Marine Object Detection Algorithm Based on SSD and Feature Enhancement

A New Method for Training CycleGAN to Enhance Images of Cold Seeps in the Qiongdongnan Sea

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