A Lightweight SAR Image Ship Detection Method Based on Improved Convolution and YOLOv7

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2024-01, Vol.16 (3), p.486
• Main Authors: Tang, Hongdou, Gao, Song, Li, Song, Wang, Pengyu, Liu, Jiqiu, Wang, Simin, Qian, Jiang
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial satellites in remote sensing; Clutter; Convolution; Datasets; Deep learning; Design; Electromagnetism; Feature maps; High resolution; Image acquisition; Image resolution; Lightweight; lightweight networks; Methods; Neural networks; Performance evaluation; Semantics; Semiconductor industry; ship detection; Ships; Synthetic aperture radar; synthetic aperture radar (SAR); Target detection; YOLOv7
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16030486

The airborne and satellite-based synthetic aperture radar enables the acquisition of high-resolution SAR oceanographic images in which even the outlines of ships can be identified. The detection of ship targets from SAR images has a wide range of applications. Due to the density of ships in SAR images, the extreme imbalance between foreground and background clutter, and the diversity of target sizes, achieving lightweight and highly accurate multi-scale ship target detection remains a great challenge. To this end, this paper proposed an attention mechanism for multi-scale receptive fields convolution block (AMMRF). AMMRF not only makes full use of the location information of the feature map to accurately capture the regions in the feature map that are useful for detection results, but also effectively captures the relationship between the feature map channels, so as to better learn the relationship between the ship and the background. Based on this, a new YOLOv7-based ship target detection method, You Only Look Once SAR Ship Identification (YOLO-SARSI), was proposed, which acquires the abstract semantic information extracted from the high-level convolution while retaining the detailed semantic information extracted from the low-level convolution. Compared to the deep learning detection methods proposed by previous authors, our method is more lightweight, only 18.43 M. We examined the effectiveness of our method on two SAR image public datasets: the High-Resolution SAR Images Dataset (HRSID) and the Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-V1.0). The results show that the average accuracy (AP50) of the detection method YOLO-SARSI proposed in this paper on the HRSID and LS-SSDD-V1.0 datasets is 2.6% and 3.9% higher than that of YOLOv7, respectively.