Energy distance based panoptic segmentation for unsupervised anomaly detection in aquatic environments

Abstract

Detecting anomalous objects in aquaculture ponds is an important but challenging problem due to the lack of labeled data. This study proposes an unsupervised learning method combining Panoptic Segmentation, Energy Distance, and Point of Interest (PoI) to automatically detect anomalous objects from pond images. The system extracts a 12-dimensional feature vector specialized for aquatic environments and calculates an Energy Distance Map measuring feature distribution differences between image regions. A two-step segmentation strategy is applied: segmenting objects with low-to-medium energy, then creating instances for high-energy regions corresponding to potential anomalous objects. Classification is based on dynamic thresholds from the 75th percentile, allowing adaptation to different image conditions. Experiments on three diverse scenarios with varying density and lighting showed promising localization results, demonstrating feasibility for smart aquaculture applications.