SAR ship detection based on improved YOLOv5 and BiFPNopen access
- Authors
- YU CHUSHI; Shin Yoan
- Issue Date
- Feb-2024
- Publisher
- ELSEVIER
- Keywords
- Synthetic aperture radarShip detectionYOLOv5Coordinate attention blockBidirectional feature pyramid network
- Citation
- ICT Express, v.10, no.1, pp 28 - 33
- Pages
- 6
- Journal Title
- ICT Express
- Volume
- 10
- Number
- 1
- Start Page
- 28
- End Page
- 33
- URI
- https://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/49394
- DOI
- 10.1016/j.icte.2023.03.009
- ISSN
- 2405-9595
- Abstract
- Synthetic aperture radar (SAR) is an advanced microwave sensor widely used in ocean monitoring, whose operation is not affected by light and weather. Ship targets in SAR images contain characteristically unclear contour information, a complex background, and display strong scattering. Ship detection algorithms based on convolutional neural networks achieved good results, albeit with many missed and false detections. To address this issue, we propose an improved scheme based on YOLOv5, that combines coordinate attention blocks and uses a bidirectional feature pyramid network for better feature fusion. Experimental results obtained with SAR images datasets demonstrate the effectiveness and applicability of the proposed model when applied for ship detection in SAR images. Compared to the original YOLOv5, the detection accuracy of the proposed method was increased from 81.28% to 88.27%, and the mean average precision was increased from 92.57% to 95.02%, which showed significant performance improvement by the proposed method in terms of detection accuracy and speed.
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