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Ship Target Detection Algorithm Based on Improved YOLOv3 for Maritime Image
- Source :
- Journal of Advanced Transportation, Vol 2021 (2021)
- Publication Year :
- 2021
- Publisher :
- Hindawi Limited, 2021.
-
Abstract
- Accurate identification of ships is the key technology of intelligent transportation in water. At the same time, it also provides a judgment basis for water traffic safety control. This paper proposed a detection method of ships in water based on improved You Only Look Once version 3 (YOLOv3), which is called Feature Attention, Feature Enhancement YOLOv3 (AE-YOLOv3). The feature attention module was constructed by introducing the attention mechanism, which was embedded in Darknet-53 for feature recalibration, which improved the feature extraction ability of the model in the complex navigable background. For the problem of insufficient semantic information of low-level features in the feature fusion process, a feature enhancement module was constructed and applied to the feature fusion part to enhance the receptive field size of the corresponding feature layer and the correlation degree of feature extraction network. Experiments were carried out on the public SeaShips dataset. Experiments show that the detection accuracy is as high as 98.72%, which is better than that of other mainstream ship identification models, fully verifying the superiority of this method in the detection of waterborne traffic ships.
- Subjects :
- Economics and Econometrics
TA1001-1280
Article Subject
Computer science
business.industry
Strategy and Management
Mechanical Engineering
Feature extraction
Process (computing)
Pattern recognition
Tracking system
Computer Science Applications
Transportation engineering
Identification (information)
Feature (computer vision)
Automotive Engineering
Key (cryptography)
Artificial intelligence
Layer (object-oriented design)
business
Transportation and communications
Intelligent transportation system
HE1-9990
Subjects
Details
- ISSN :
- 20423195 and 01976729
- Volume :
- 2021
- Database :
- OpenAIRE
- Journal :
- Journal of Advanced Transportation
- Accession number :
- edsair.doi.dedup.....d7b04226fa3e089df15184b014af2d84