Coordinate-Aware Mask R-CNN with Group Normalization: A underwater marine animal instance segmentation framework
Dewei Yi, Hasan Bayarov Ahmedov, Shouyong Jiang, Yiren Li, Sean Joseph Flinn, Paul G. Fernandes
Abstract
Unsustainable fishing, driven by bycatch and discards, harms marine ecosystems. Addressing this, we propose a Coordinate-Aware Mask R-CNN (CAM-RCNN) method to enhance fish detection in commercial trawls. Leveraging CoordConv and Group Normalization, our approach improves generalization and stability. To tackle class imbalance, a compound Dice and cross-entropy loss is employed, and image data are enhanced through multi-scale retinex and color restoration. Evaluating on two fishing datasets, CAM-RCNN excels in accuracy and generalization, achieving the best Average Precision (AP) for instance mask and BBOX prediction in both source (39.7%, 40.2%) and target domains (24.4%, 24.2%). This method promotes sustainable fishing by selectively capturing desired fish, reducing harm to non-target species.