Optimized YOLOv8s framework with deformable convolution for underwater object detection
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces O-YOLOv8s-DC, an optimized deep learning model for underwater object detection. It significantly improves the detection of small and occluded targets in challenging aquatic conditions.
Area Of Science
- Computer Vision
- Marine Technology
- Artificial Intelligence
Background
- Underwater object detection is crucial for the growing aquatic economy.
- Challenges include small/occluded targets, varying object shapes, and poor image quality due to turbidity.
Purpose Of The Study
- To develop an optimized deep learning framework, O-YOLOv8s-DC, for enhanced underwater object detection.
- To address limitations of existing models in detecting small, occluded, and morphologically diverse underwater objects.
Main Methods
- Proposed O-YOLOv8s-DC framework integrating deformable convolution (C2f_DC), depth-weighted bidirectional feature pyramid (DeepBiFPN), content-aware feature reorganization (CARAFE), and efficient multi-scale attention (EMA).
- Conducted ablation studies to validate individual module contributions.
- Evaluated performance on LFIW and OI datasets.
Main Results
- O-YOLOv8s-DC significantly outperformed mainstream models like SSD, YOLOv8s, and DETR.
- Achieved a higher AP@[0.50:0.05:0.95] compared to the original YOLOv8s.
- Demonstrated enhanced performance for occluded targets at strict IoU thresholds (e.g., AP@0.75) and improved small-target recognition accuracy.
Conclusions
- O-YOLOv8s-DC provides reliable underwater object detection in complex environments.
- Offers technical support for aquatic ecological protection and sustainable underwater operations.
- The integrated modules effectively tackle challenges in underwater object detection.
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