Data Augmentation for Enhanced Fish Detection in Lake Environments: Affine Transformations, Neural Filters, SinGAN

Abstract

Understanding fish habitats is essential for fisheries management, habitat restoration, and species protection. Automated fish detection is a key tool in these applications, which enables real-time monitoring and quantitative analysis. Recent advancements in high-resolution cameras and machine learning technologies have facilitated image analysis automation, promoting remote fish tracking. However, many of these detection methods require large volumes of annotated data, which involve considerable effort and time. Additionally, their practical implementation remains challenging in environments with limited data. Hence, this study proposes an anomaly-based fish detection approach by integrating Patch Distribution Modeling with data augmentation techniques, including Affine Transformations, Neural Filters, and SinGAN. Field experiments were conducted in Lake Izunuma-Uchinuma, Japan, using an electrofishing boat to acquire data. Evaluation metrics, such as AUROC and F1-score, assessed detection performance. The results indicate that, compared to the original dataset (AUROC: 0.836, F1-score: 0.483), Neural Filters (AUROC: 0.940, F1-score: 0.879) and Affine Transformations (AUROC: 0.942, F1-score: 0.766) improve anomaly detection. However, SinGAN exhibited no measurable enhancement, indicating the necessity for further optimization. This shows the potential of the proposed approach to enhance automated fish detection in limited-data environments, supporting aquatic ecosystem sustainability.