AASNet: A Novel Image Instance Segmentation Framework for Fine-Grained Fish Recognition via Linear Correlation Attention and Dynamic Adaptive Focal Loss
Smart fisheries, integrating advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and image processing, are pivotal in enhancing aquaculture efficiency, sustainability, and resource management by enabling real-time environmental monitoring, precision feeding, and...
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/7/3986 |
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| author | Jianlei Kong Shunong Tang Jiameng Feng Lipo Mo Xuebo Jin |
| author_facet | Jianlei Kong Shunong Tang Jiameng Feng Lipo Mo Xuebo Jin |
| author_sort | Jianlei Kong |
| collection | DOAJ |
| description | Smart fisheries, integrating advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and image processing, are pivotal in enhancing aquaculture efficiency, sustainability, and resource management by enabling real-time environmental monitoring, precision feeding, and disease prevention. However, underwater fish recognition faces challenges in complex aquatic environments, which hinder accurate detection and behavioral analysis. To address these issues, we propose a novel image instance segmentation framework based on a deep learning neural network, defined as the AASNet (Agricultural Aqua Segmentation Network). In order to improve the accuracy and real-time availability of fine-grained fish recognition, we introduce a Linear Correlation Attention (LCA) mechanism, which uses Pearson correlation coefficients to capture linear correlations between features. This helps resolve inconsistencies caused by lighting changes and color variations, significantly improving the extraction of semantic information for similar objects. Additionally, Dynamic Adaptive Focal Loss (DAFL) is designed to improve classification under extreme data imbalance conditions. Abundant experiments on two underwater datasets demonstrated that the proposed AASNet obtains an optimal balance between segmentation performance and efficiency. Concretely, AASNet achieves mAP scores of 31.7 and 47.4, respectively, on the UIIS and USIS dataset, significantly outperforming existing state-of-the-art methods. Moreover, AASNet achieves an inference image recognition speed of up to 28.9 ms/per, which is suitable for practical agricultural applications of smart fish farming. |
| format | Article |
| id | doaj-art-2ebc4fee63314b7bb944dad43e28a672 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-2ebc4fee63314b7bb944dad43e28a6722025-08-20T03:06:31ZengMDPI AGApplied Sciences2076-34172025-04-01157398610.3390/app15073986AASNet: A Novel Image Instance Segmentation Framework for Fine-Grained Fish Recognition via Linear Correlation Attention and Dynamic Adaptive Focal LossJianlei Kong0Shunong Tang1Jiameng Feng2Lipo Mo3Xuebo Jin4National Engineering Research Center for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, ChinaNational Engineering Research Center for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, ChinaNational Engineering Research Center for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, ChinaInstitute of Systems Science, Beijing Wuzi University, Beijing 101149, ChinaNational Engineering Research Center for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, ChinaSmart fisheries, integrating advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and image processing, are pivotal in enhancing aquaculture efficiency, sustainability, and resource management by enabling real-time environmental monitoring, precision feeding, and disease prevention. However, underwater fish recognition faces challenges in complex aquatic environments, which hinder accurate detection and behavioral analysis. To address these issues, we propose a novel image instance segmentation framework based on a deep learning neural network, defined as the AASNet (Agricultural Aqua Segmentation Network). In order to improve the accuracy and real-time availability of fine-grained fish recognition, we introduce a Linear Correlation Attention (LCA) mechanism, which uses Pearson correlation coefficients to capture linear correlations between features. This helps resolve inconsistencies caused by lighting changes and color variations, significantly improving the extraction of semantic information for similar objects. Additionally, Dynamic Adaptive Focal Loss (DAFL) is designed to improve classification under extreme data imbalance conditions. Abundant experiments on two underwater datasets demonstrated that the proposed AASNet obtains an optimal balance between segmentation performance and efficiency. Concretely, AASNet achieves mAP scores of 31.7 and 47.4, respectively, on the UIIS and USIS dataset, significantly outperforming existing state-of-the-art methods. Moreover, AASNet achieves an inference image recognition speed of up to 28.9 ms/per, which is suitable for practical agricultural applications of smart fish farming.https://www.mdpi.com/2076-3417/15/7/3986digital agricultureintelligent fish farming systemdeep learning neural networkunderwater image segmentationfine-grained visual recognition |
| spellingShingle | Jianlei Kong Shunong Tang Jiameng Feng Lipo Mo Xuebo Jin AASNet: A Novel Image Instance Segmentation Framework for Fine-Grained Fish Recognition via Linear Correlation Attention and Dynamic Adaptive Focal Loss Applied Sciences digital agriculture intelligent fish farming system deep learning neural network underwater image segmentation fine-grained visual recognition |
| title | AASNet: A Novel Image Instance Segmentation Framework for Fine-Grained Fish Recognition via Linear Correlation Attention and Dynamic Adaptive Focal Loss |
| title_full | AASNet: A Novel Image Instance Segmentation Framework for Fine-Grained Fish Recognition via Linear Correlation Attention and Dynamic Adaptive Focal Loss |
| title_fullStr | AASNet: A Novel Image Instance Segmentation Framework for Fine-Grained Fish Recognition via Linear Correlation Attention and Dynamic Adaptive Focal Loss |
| title_full_unstemmed | AASNet: A Novel Image Instance Segmentation Framework for Fine-Grained Fish Recognition via Linear Correlation Attention and Dynamic Adaptive Focal Loss |
| title_short | AASNet: A Novel Image Instance Segmentation Framework for Fine-Grained Fish Recognition via Linear Correlation Attention and Dynamic Adaptive Focal Loss |
| title_sort | aasnet a novel image instance segmentation framework for fine grained fish recognition via linear correlation attention and dynamic adaptive focal loss |
| topic | digital agriculture intelligent fish farming system deep learning neural network underwater image segmentation fine-grained visual recognition |
| url | https://www.mdpi.com/2076-3417/15/7/3986 |
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