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

2026-04-24 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, long

Summary

A novel data augmentation framework, Pseudo-Simulated Annealing Data Augmentation (PSADA), significantly improves dense object detection in challenging underwater environments. This framework addresses the limitations of standard YOLOv10 models, which typically struggle with high variability and frequent occlusions in natural settings. The researchers generated a custom detection dataset from the DeepFish dataset's segmentation masks and developed a pseudo–simulated annealing–based augmentation algorithm, inspired by Deng et al.'s copy-paste strategy, to synthesize realistic crowded fish scenarios. This approach enhanced spatial diversity and object density during training. Experimental results demonstrated that the PSADA model substantially outperformed a baseline YOLOv10 model, particularly on a challenging test set of 50 manually annotated images from live-stream footage in the Florida Keys, detecting more than double the fish compared to the baseline.

Key takeaway

For research scientists developing object detection models for challenging natural environments, you should consider integrating advanced data augmentation techniques like pseudo-simulated annealing. This approach can significantly improve model robustness and detection accuracy in dense, unconstrained scenes, even with limited or sparse training data. Focus on creating diverse training examples that reflect the complexity of real-world conditions, such as varied object densities and lighting, to enhance generalization.

Key insights

A pseudo-simulated annealing data augmentation method improves underwater object detection in crowded, natural scenes.

Principles

• Data augmentation can overcome dataset limitations.
• Simulated annealing principles enhance object placement diversity.

Method

The method involves generating bounding boxes from segmentation masks, then applying a modified copy-paste algorithm with Poisson-sampled group centers and simulated annealing for object placement to create diverse, crowded training images.

In practice

• Use segmentation masks to generate bounding boxes for detection.
• Apply copy-paste augmentation for crowded scene robustness.
• Test models on real-world, diverse live-stream data.

Topics

• Dense Object Detection
• Data Augmentation
• Pseudo-Simulated Annealing
• YOLOv10
• DeepFish Dataset

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.