Ergün Ebru
Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Recep Tayyip Erdogan University, Rize, Turkey.
PLoS One. 2025 May 20;20(5):e0322711. doi: 10.1371/journal.pone.0322711. eCollection 2025.
The fish market is a crucial industry for both domestic economies and the global seafood trade. Accurate fish species classification (FSC) plays a significant role in ensuring sustainability, improving food safety, and optimizing market efficiency. This study introduces automatic FSC using Swin Transformer (ST) through transfer learning (SwinFishNet), which proposes an innovative approach to FSC by leveraging the ST model, a cutting-edge architecture known for its exceptional performance in computer vision tasks. The ST's unique ability to capture both local and global features through its hierarchical structure enhances its effectiveness in complex image classification tasks. The model utilizes three distinct datasets: the 12-class BD-Freshwater-Fish dataset, the 10-class SmallFishBD dataset, and the 20-class FishSpecies dataset, focusing on image processing-based classification. Images were preprocessed by resizing to 224 [Formula: see text] 224 pixels, normalizing, and converting to tensor format for compatibility with deep learning models. Transfer learning was applied using the ST, which was fine-tuned on these datasets and optimized with the AdamW algorithm. The model's performance was evaluated using classification accuracy (CA), F1-score, recall, precision, Matthews correlation coefficient, Cohen's kappa and confusion matrix metrics. The results yielded promising CAs: 0.9847 for BD-Freshwater-Fish, 0.9964 for SmallFishBD, and 0.9932 for the FishSpecies dataset. These results underscore the potential of the SwinFishNet in automating FSC and demonstrate its significant contributions to improving sustainability, market efficiency, and food safety in the seafood industry. This work offers a novel methodology with broad applications in both commercial and research settings, advancing the role of artificial intelligence in the fish market.
鱼类市场对于国内经济和全球海鲜贸易而言都是至关重要的产业。准确的鱼类物种分类（FSC）在确保可持续性、提高食品安全以及优化市场效率方面发挥着重要作用。本研究通过迁移学习（SwinFishNet）引入了使用Swin Transformer（ST）的自动FSC，该方法通过利用ST模型提出了一种创新的FSC方法，ST是一种在计算机视觉任务中具有卓越性能的前沿架构。ST通过其分层结构捕捉局部和全局特征的独特能力增强了其在复杂图像分类任务中的有效性。该模型使用了三个不同的数据集：12类的BD-淡水鱼数据集、10类的SmallFishBD数据集和20类的鱼类物种数据集，专注于基于图像处理的分类。图像经过预处理，调整大小为224×224像素，进行归一化，并转换为张量格式以与深度学习模型兼容。使用ST应用迁移学习，在这些数据集上进行微调，并使用AdamW算法进行优化。使用分类准确率（CA）、F1分数、召回率、精确率、马修斯相关系数、科恩kappa系数和混淆矩阵指标对模型性能进行评估。结果产生了令人满意的CA：BD-淡水鱼数据集为0.9847，SmallFishBD数据集为0.9964，鱼类物种数据集为0.9932。这些结果强调了SwinFishNet在自动化FSC方面的潜力，并证明了其对提高海鲜行业的可持续性、市场效率和食品安全的重大贡献。这项工作提供了一种在商业和研究环境中都有广泛应用的新方法，推进了人工智能在鱼类市场中的作用。
