A Deep Transformer Model-Based Analysis of Fish School Starvation Degree in Marine Farming Vessels

Zheng, Kaijian; Yang, Renyou; Li, Rifu; Yang, Liang; Qin, Hao; Sun, Min

doi:10.1109/iccr55715.2022.10053891

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Zheng [19] and colleagues utilized a Spatio-Temporal Attention Network (STAN) to analyze the feeding behavior of Pompano fish stocks. By innovatively combining spatial images [20] with optical flow images [21][22][23] for video analysis, the study applied the STAN model to extract intuitive and perceptual features to determine the feeding or non-feeding states of Pompano fish stocks. This method incorporated a hierarchical convolutional network (HCN) to extract multi-scale spatial features.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet

Feng,

Kan,

Chen

2024

Applied Sciences

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To enhance the accuracy of lightweight CNN classification models in analyzing fish feeding behavior, this paper addresses the image quality issues caused by external environmental factors and lighting conditions, such as low contrast and uneven illumination, by proposing a Multi-step Image Pre-enhancement Strategy (MIPS). This strategy includes three critical steps: initially, images undergo a preliminary processing using the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm, effectively reducing the impact of water surface reflections and enhancing the visual effect of the images; secondly, the Multi-Metric-Driven Contrast Limited Adaptive Histogram Equalization (mdc) technique is applied to further improve image contrast, especially in areas of low contrast, by adjusting the local contrast levels to enhance the clarity of the image details; finally, Unsharp Masking (UM) technology is employed to sharpen the images, emphasizing their edges to increase the clarity of the image details, thereby significantly improving the overall image quality. Experimental results on a custom dataset have confirmed that this pre-enhancement strategy significantly boosts the accuracy of various CNN-based classification models, particularly for lightweight CNN models, and drastically reduces the time required for model training compared to the use of advanced ResNet models. This research provides an effective technical route for improving the accuracy and efficiency of an image-based analysis of fish feeding behavior in complex environments.

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Section: Introductionmentioning

confidence: 99%

A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet

Feng,

Kan,

Chen

2024

Applied Sciences

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A dual stream hierarchical transformer for starvation grading of golden pomfret in marine aquaculture

Zheng

Yang

et al. 2022

Front. Mar. Sci.

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IntroductionFish starvation grading can provide feeding information for aquaculture, reducing the cost of lures and helping to promote the unmanned and intelligent process of offshore aquaculture.MethodsIn this study, we used golden pompano as the experimental object to address the fish starvation grading problem in the marine culture vessel environment, and proposed the dual stream hierarchical transformer to provide additional temporal information for the starvation grading task, which improved the grading accuracy. We first built a dual stream dataset with both spatial and temporal channel, and divided the fish school starvation status into five levels (very bloated, a little bloated, modest, a little starving, very starving) according to the feeding time and experience. Based on the marine image characteristics, we proposed a dual stream hierarchical transformer with hierarchical convolutional network, composite fusion convolution and transformer.Results and discussionWe finally evaluated the efficacy of the model based on qualitative and quantitative analyses, revealing that the proposed dual stream hierarchical transformer achieved the state-of-the-art starvation grading performance with a test accuracy of 98.05%. Our model outperformed other mainstream models, including VGG, ResNet, attentionbased model and other fish status grading related model. Field tests on the vessel further suggested that the model can be applied to the mariculture environment of golden pomfret.

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A Deep Transformer Model-Based Analysis of Fish School Starvation Degree in Marine Farming Vessels

Cited by 2 publications

References 15 publications

A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet

A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet

A dual stream hierarchical transformer for starvation grading of golden pomfret in marine aquaculture

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