A Probabilistic Framework for Improving Dense Object Detection in Underwater Image Data via Annealing-Based Data Augmentation

TL;DR

This paper introduces a novel annealing-based data augmentation method to enhance dense object detection in underwater images, significantly improving model robustness and accuracy in complex, real-world underwater scenes.

Contribution

The authors propose a pseudo-simulated annealing augmentation algorithm inspired by copy-paste strategies, tailored for underwater object detection to improve generalization in dense scenes.

Findings

Outperforms baseline YOLOv10 on underwater datasets

Enhances detection accuracy in dense, occluded underwater scenes

Improves model robustness to variability in underwater environments

Abstract

Object detection models typically perform well on images captured in controlled environments with stable lighting, water clarity, and viewpoint, but their performance degrades substantially in real-world underwater settings characterized by high variability and frequent occlusions. In this work, we address these challenges by introducing a novel data augmentation framework designed to improve robustness in dense and unconstrained underwater scenes. Using the DeepFish dataset, which contains images of fish in natural environments, we first generate bounding box annotations from provided segmentation masks to construct a custom detection dataset. We then propose a pseudo-simulated annealing-based augmentation algorithm, inspired by the copy-paste strategy of Deng et al. [1], to synthesize realistic crowded fish scenarios. Our approach improves spatial diversity and object density during training, enabling better generalization to complex scenes. Experimental results show that our method significantly outperforms a baseline YOLOv10 model, particularly on a challenging test set of manually annotated images collected from live-stream footage in the Florida Keys. These results demonstrate the effectiveness of our augmentation strategy for improving detection performance in dense, real-world underwater environments.