An adaptive image enhancement method for a recirculating aquaculture system

Zhou, Chao; Yang, Xinting; Zhang, Baihai; Kai, Lin; Xu, Daming; Guo, Qiang; Sun, Cheng

doi:10.1038/s41598-017-06538-9

Cited by 27 publications

(23 citation statements)

References 92 publications

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“…However, the scenes encountered in aquaculture present numerous challenges for image and video analysis. First, the image quality is easily affected by light, noise and water turbidity, resulting in relatively low resolution and contrast (Zhou et al 2017a). Second, because fish swim freely and are uncontrolled targets, their behaviour may cause distortions, deformations, occlusion, overlapping and other disadvantageous phenomena (Zhou et al 2017b).…”

Section: Live Fish Identificationmentioning

confidence: 99%

Deep learning for smart fish farming: applications, opportunities and challenges

Yang

Zhang

Liu

et al. 2020

Reviews in Aquaculture

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213

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The rapid emergence of deep learning (DL) technology has resulted in its successful use in various fields, including aquaculture. DL creates both new opportunities and a series of challenges for information and data processing in smart fish farming. This paper focuses on applications of DL in aquaculture, including live fish identification, species classification, behavioural analysis, feeding decisions, size or biomass estimation, and water quality prediction. The technical details of DL methods applied to smart fish farming are also analysed, including data, algorithms and performance. The review results show that the most significant contribution of DL is its ability to automatically extract features. However, challenges still exist; DL is still in a weak artificial intelligence stage and requires large amounts of labelled data for training, which has become a bottleneck that restricts further DL applications in aquaculture. Nevertheless, DL still offers breakthroughs for addressing complex data in aquaculture. In brief, our purpose is to provide researchers and practitioners with a better understanding of the current state of the art of DL in aquaculture, which can provide strong support for implementing smart fish farming applications.

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Section: Live Fish Identificationmentioning

confidence: 99%

Deep learning for smart fish farming: applications, opportunities and challenges

Yang

Zhang

Liu

et al. 2020

Reviews in Aquaculture

Self Cite

213

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“…In some aquaculture facilities, light conditions are usually insufficient, resulting in images with low brightness and high contrast (Zhou et al . ); however, near‐infrared computer vision technology is not affected by visible light intensity and can yield good imaging results in environments with relatively dim light (Farokhi et al . ; Hung et al .…”

Section: Feeding Control Methods Based On Computer Visionmentioning

confidence: 99%

“…In agriculture, its application has been extended to obtaining crop growth data, nondestructive testing of agricultural products and other aspects. In some aquaculture facilities, light conditions are usually insufficient, resulting in images with low brightness and high contrast (Zhou et al 2017a); however, near-infrared computer vision technology is not affected by visible light intensity and can yield good imaging results in environments with relatively dim light (Farokhi et al 2016;Hung et al 2016). Furthermore, the cost of this technology is also very low, and the methods are easy to develop.…”

Section: Individual Feature Analysismentioning

confidence: 99%

Intelligent feeding control methods in aquaculture with an emphasis on fish: a review

Zhou

Kai

et al. 2017

Reviews in Aquaculture

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142

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In aquaculture, feeding is the primary factor determining efficiency and cost, so it is important to know when to stop feeding to maximize efficiency. Until now, fish feeding has been mostly based on artificial discrimination, which is usually time‐consuming and laborious. In recent years, intelligent feeding control according to changes in behaviour and growth status has gained increasing attention. This approach involves many methods as well as monitoring and feedback equipment and can automatically determine the feeding demands of fish. This review summarizes the development of intelligent feeding control methods, such as mathematical models, acoustic methods and computer vision, in aquaculture over the past three decades. All methods have unique application scenarios and models for the culture to which they are most suitable, and the advantages and disadvantages of each method in the laboratory as well as in pond, cage and recirculating aquaculture systems are analysed. Studies show that improvements in sensor accuracy and hardware and software processing speed have promoted the development of new technologies and methods, providing effective or potential support for intelligent feeding control. However, its accuracy and intelligent are still need to be improved to meet the needs of actual feeding scenarios. Through close collaborations between engineers and fish behaviourists, the feeding machine and system will be more elaborate and precise on the basis of the above methods, and the level of intelligence will be further improved.

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“…Although CNN-based SISR models have achieved breakthroughs in both accuracy and speed, the images they reconstruct lack fine textural details for images with large sampling factors [24]- [26]. These SISR models are driven by the objective loss function.…”

Section: Related Work a Generative Adversarial Networkmentioning

confidence: 99%

Image Super-Resolution Reconstruction Using Generative Adversarial Networks Based on Wide-Channel Activation

et al. 2020

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In recent years, residual learning has shown excellent performance on convolutional neural network (CNN)-based single-image super-resolution (SISR) tasks. However, CNN-based SISR approaches have focused mainly on the design of deep architectures, and the rectified linear units (ReLUs) used in these networks hinder shallow-to-deep information transfer. As a result, these methods are unable to utilize some shallow information, and improving model performance is difficult. To solve the above issues, this paper proposes an image SR reconstruction method based on a generative adversarial network with a residual dense architecture. First, before ReLU activation, the number of feature channels is expanded by a factor of 6∼9 using a 1 × 1 convolutional layer, which improves the utilization of shallow information. Next, the original discriminator is replaced with a relativistic average discriminator, thereby improving the authenticity of the discriminative network. Finally, preactivation features are used to improve the perceptual loss, thus providing stronger monitoring for brightness consistency and texture restoration. Experimental results show that the proposed algorithm improves the utilization of shallow information in a deep network. Structural similarity (SSIM) index evaluations show that the overall utilization of shallow information is increased by 105.52%. In addition, the average runtime is 0.42 sec/frame, nearly 3.6 times faster than those of traditional methods. Moreover, the recovered images have an average natural image quality evaluator value of 3.4 and high perceptual quality, showing that the proposed method is suitable for image reconstruction applications in fields such as agriculture and medicine. INDEX TERMS Super-resolution, residual block, relativistic average discriminator, generative adversarial network, perceived quality.

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An adaptive image enhancement method for a recirculating aquaculture system

Cited by 27 publications

References 92 publications

Deep learning for smart fish farming: applications, opportunities and challenges

Deep learning for smart fish farming: applications, opportunities and challenges

Intelligent feeding control methods in aquaculture with an emphasis on fish: a review

Image Super-Resolution Reconstruction Using Generative Adversarial Networks Based on Wide-Channel Activation

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