A Multi-Tiered Collaborative Network for Optical Remote Sensing Fine-Grained Ship Detection in Foggy Conditions

Ship target detection faces the challenges of complex and changing environments combined with the varied characteristics of ship targets. In practical applications, the complexity of meteorological conditions, uncertainty of lighting, and the diversity of ship target characteristics can affect the a...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-11, Vol.16 (21), p.3968
Main Authors: Zhou, Wenbo, Li, Ligang, Liu, Bo, Cao, Yuan, Ni, Wei
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Changing environments
Collaboration
collaborative network
Complexity
Datasets
Deep learning
detection of ship targets
Environmental changes
foggy weather conditions
Frames per second
Image contrast
Image enhancement
Image quality
low-quality images
Real time
Remote sensing
Sensors
Target detection
Weather
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Summary:Ship target detection faces the challenges of complex and changing environments combined with the varied characteristics of ship targets. In practical applications, the complexity of meteorological conditions, uncertainty of lighting, and the diversity of ship target characteristics can affect the accuracy and efficiency of ship target detection algorithms. Most existing target detection methods perform well in conditions of a general scenario but underperform in complex conditions. In this study, a collaborative network for target detection under foggy weather conditions is proposed, aiming to achieve improved accuracy while satisfying the need for real-time detection. First, a collaborative block was designed and SCConv and PCA modules were introduced to enhance the detection of low-quality images. Second, the PAN + FPN structure was adopted to take full advantage of its lightweight and efficient features. Finally, four detection heads were used to enhance the performance. In addition to this, a dataset for foggy ship detection was constructed based on ShipRSImageNet, and the mAP on the dataset reached 48.7%. The detection speed reached 33.3 frames per second (FPS), which is ultimately comparable to YOLOF. It shows that the model proposed has good detection effectiveness for remote sensing ship images during low-contrast foggy days.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16213968