Lightweight algorithm for multi-scale ship detection based on high-resolution SAR images. Issue 4 (16th February 2023)
- Record Type:
- Journal Article
- Title:
- Lightweight algorithm for multi-scale ship detection based on high-resolution SAR images. Issue 4 (16th February 2023)
- Main Title:
- Lightweight algorithm for multi-scale ship detection based on high-resolution SAR images
- Authors:
- Kong, Weimin
Liu, Shanwei
Xu, Mingming
Yasir, Muhammad
Wang, Dawei
Liu, Wantao - Abstract:
- ABSTRACT: As ship target detection technology has high application value in military and civil fields, it is significant to research ship detection in SAR images. Aiming at the complex and diverse backgrounds, significant differences in ship sizes, and real-time detection problems in the ship target detection task of SAR remote sensing images, a lightweight ship detection network based on the YOLOx-Tiny model is proposed. Firstly, a multi-scale ship feature extraction module is proposed, composed of a parallel multi-branch structure connected by a standard convolution layer, asymmetric convolution layer, and dilatation convolution layer with different expansion rates in turn. It makes better use of local features and global features and effectively improves the detection accuracy of multi-scale ship targets; Secondly, to ensure detection performance and eliminate background interference, we propose a whole SAR remote sensing image detection strategy based on an adaptive threshold, which effectively suppresses false alarms caused by background and improves detection speed. The experimental results on two different SAR ship datasets, SSDD and HRSID, show that, compared with several advanced methods, the effectiveness and superiority of the method in this paper are verified, and excellent results are shown in the detection of the whole SAR remote-sensing image. It can provide effective theoretical and technical support for ship detection on platforms with limited computingABSTRACT: As ship target detection technology has high application value in military and civil fields, it is significant to research ship detection in SAR images. Aiming at the complex and diverse backgrounds, significant differences in ship sizes, and real-time detection problems in the ship target detection task of SAR remote sensing images, a lightweight ship detection network based on the YOLOx-Tiny model is proposed. Firstly, a multi-scale ship feature extraction module is proposed, composed of a parallel multi-branch structure connected by a standard convolution layer, asymmetric convolution layer, and dilatation convolution layer with different expansion rates in turn. It makes better use of local features and global features and effectively improves the detection accuracy of multi-scale ship targets; Secondly, to ensure detection performance and eliminate background interference, we propose a whole SAR remote sensing image detection strategy based on an adaptive threshold, which effectively suppresses false alarms caused by background and improves detection speed. The experimental results on two different SAR ship datasets, SSDD and HRSID, show that, compared with several advanced methods, the effectiveness and superiority of the method in this paper are verified, and excellent results are shown in the detection of the whole SAR remote-sensing image. It can provide effective theoretical and technical support for ship detection on platforms with limited computing resources and has good application prospects. … (more)
- Is Part Of:
- International journal of remote sensing. Volume 44:Issue 4(2023)
- Journal:
- International journal of remote sensing
- Issue:
- Volume 44:Issue 4(2023)
- Issue Display:
- Volume 44, Issue 4 (2023)
- Year:
- 2023
- Volume:
- 44
- Issue:
- 4
- Issue Sort Value:
- 2023-0044-0004-0000
- Page Start:
- 1390
- Page End:
- 1415
- Publication Date:
- 2023-02-16
- Subjects:
- extreme learning machine -- ship detection -- SAR
Remote sensing -- Periodicals
Télédétection -- Périodiques
621.3678 - Journal URLs:
- http://www.tandfonline.com/toc/tres20/current ↗
http://www.tandfonline.com/ ↗ - DOI:
- 10.1080/01431161.2023.2182652 ↗
- Languages:
- English
- ISSNs:
- 0143-1161
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4542.528000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 26291.xml