Year: 2024
Type: conference-paper
Abstract: With the development of China's shipping industry, effective maritime management requires a sound monitoring system. This article improves YOLOv11 for ship detection in lightweight SAR images, with a focus on small ship targets and complex port environments. We added a visual converter to the feature extraction network to enhance semantic understanding, introduced a small object detection layer with additional detection heads to better capture small object features, and designed a custom loss function for sparse backgrounds based on SIoU. Our experimental results on SSDD and HRSID datasets demonstrate that, our method improved accuracy by 2.5% and recall by 6.1% compared to YOLOv11, with only an increase of 0.2MB. This method significantly improves the ship detection and maritime monitoring capabilities.
Authors: Yuchuan Wang, Jitao Liu, Xi Wang, Jin He, Yao Tong, Jinjia Ruan
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4409832792