Adaptive identity-regularized generative adversarial networks with species-specific loss functions for enhanced fish classification and segmentation through data augmentation

Traditional fish classification systems suffer from limited training data and imbalanced datasets, particularly for rare or morphologically complex species. This paper presents a novel Generative Adversarial Network architecture that integrates adaptive identity blocks to preserve critical species-specific features during generation, coupled with species-specific loss functions designed around distinctive characteristics of marine species. Our method introduces adaptive identity blocks that learn to maintain species-invariant features while allowing controlled morphological variations for data augmentation. The species-specific loss function incorporates morphological constraints and taxonomic relationships to ensure generated samples maintain biological plausibility while enhancing dataset diversity. Experimental evaluation on a comprehensive fish dataset containing nine species demonstrated significant performance improvements. Our proposed method achieved 95.1% ± 1.0% classification accuracy, representing a 9.7% improvement over baseline methods and 6.7% improvement over traditional augmentation approaches. While demonstrated on a dataset of 9000 images across nine fish species, these results provide a solid foundation that warrants validation on larger, more taxonomically diverse datasets to establish broader generalizability. Segmentation performance achieved 89.6% ± 1.3% mean Intersection over Union, representing a 12.3% improvement over baseline methods. Critically, our approach showed substantial improvements for morphologically complex species, with expert evaluation by marine biology specialists confirming 88.7% ± 2.0% overall quality and achieving 87.4% ± 1.6% biological validation score. Statistical significance testing confirmed all improvements at p < 0.001 with large effect sizes, and cross-validation demonstrated exceptional consistency across folds. The results validate the effectiveness of our biologically-informed approach for generating high-quality synthetic fish data that significantly improves classification and segmentation performance while maintaining biological authenticity.