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A fish image segmentation methodology in aquaculture environment based on multi-feature fusion model
Dashe Li1, Yufang Yang1, Siwei Zhao1
1School of Computer Science and Technology, Shandong Technology and Business University, China.
Marine Environmental Research
|July 18, 2023
Summary
This study introduces a new convolutional neural network model for segmenting underwater fish images, improving accuracy in challenging marine environments. The developed model enhances feature extraction for better intelligent aquaculture applications.
Area of Science:
- Computer Vision
- Marine Biology
- Artificial Intelligence
Background:
- Underwater fish image processing is crucial for intelligent aquaculture but faces challenges like color cast, low contrast, and blur.
- Existing segmentation methods lack adaptive models, leading to suboptimal accuracy in complex marine environments.
Purpose of the Study:
- To develop an advanced convolutional neural network (CNN) model for accurate underwater fish image segmentation.
- To address limitations in current methods by proposing adaptive preprocessing and feature extraction modules.
Main Methods:
- A novel fish image preprocessing technique using pixel thresholding and minimum Euclidean distance for feature enhancement.
- Introduction of a multiscale attentional feature extraction module (MAFEM) integrating adaptive channel attention and dilated convolutional pyramid pooling.
- Training and validation of the model using a custom VOC-format dataset of underwater fish images.
Main Results:
- The proposed model achieved a mean intersection over union (MIoU) of 92.6% on the underwater fish image dataset.
- Demonstrated significant improvements over traditional models, with average increases of 1.84% in MIoU, 0.785% in mean pixel accuracy (MPA), and 1.18% in F1-score.
- The MAFEM module effectively strengthens the extraction of high-level semantic features.
Conclusions:
- The developed CNN model offers superior segmentation performance for underwater fish images compared to existing methods.
- Provides a robust foundation for intelligent monitoring systems, including fish body length measurement, weight estimation, and health status assessment.
