Infrared Ship Detection in Complex Nearshore Scenes Based on Improved YOLOv5s
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces CGSE-YOLOv5s, an improved algorithm for ship detection in complex nearshore infrared images. It enhances accuracy in challenging environments, crucial for maritime safety and shipping.
Area Of Science
- Maritime technology
- Computer vision
- Remote sensing
Background
- Navigational safety in nearshore waters is vital for the shipping economy.
- Ship detection technology faces challenges in dense, complex nearshore environments.
- Infrared vision offers superior performance over visible light for all-weather target detection.
Purpose Of The Study
- To develop an advanced ship detection algorithm for complex nearshore infrared scenarios.
- To address limitations of existing methods in dense vessel distributions and cluttered backgrounds.
- To improve the accuracy and reliability of ship identification and classification.
Main Methods
- Proposed CGSE-YOLOv5s algorithm, an enhancement of YOLOv5s.
- Incorporated Contrast Limited Adaptive Histogram Equalization with Gaussian Filtering for edge enhancement.
- Replaced C3 module with Swin Transformer-based C3STR module to reduce multi-scale false detections.
- Implemented Efficient Channel Attention mechanism to amplify target features.
Main Results
- CGSE-YOLOv5s achieved a mean average precision (mAP@0.5) of 94.8%.
- Demonstrated a 1.3% improvement over the standard YOLOv5s.
- Outperformed other existing ship detection algorithms in nearshore infrared scenarios.
Conclusions
- The proposed CGSE-YOLOv5s algorithm significantly enhances ship detection in complex nearshore infrared environments.
- The integrated enhancements effectively address challenges posed by dense vessel distributions and complex backgrounds.
- This technology contributes to improved navigational safety and sustainable development of the shipping economy.
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