[1]WANG Dewen,SONG Xueshuai,LI Chenghao,et al.Ship detection in remote sensing images using edge enhancement and multi-scale feature fusion[J].CAAI Transactions on Intelligent Systems,2026,21(1):60-71.[doi:10.11992/tis.202505014]
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Ship detection in remote sensing images using edge enhancement and multi-scale feature fusion

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 1 Page number: 60-71 Column: 学术论文—机器学习 Public date: 2026-03-05
Title:
Ship detection in remote sensing images using edge enhancement and multi-scale feature fusion
Author(s):
WANG Dewen1; 2;  SONG Xueshuai1;  LI Chenghao1;  ZHAO Wenqing1; 3
1. Department of Computer, North China Electric Power University, Baoding 071003, China;
2. Hebei Key Laboratory of Knowledge Computing for Energy & Power, Baoding 071003, China;
3. Engineering Research Center of Intelligent Computing for Complex Energy Systems, Ministry of Education, Baoding 071003, China
Keywords:
remote sensing image; ship detection; high-frequency feature; edge enhancement; multi-scale; feature fusion; lightweight detection head; YOLO
CLC:
TP751
DOI:
10.11992/tis.202505014
Abstract:
Ship objects in remote sensing images exhibit large scale variation, dense distribution, and arbitrary orientation. In particular, the low contrast between ships and the ocean background, along with blurred boundaries between adjacent ships, poses greater challenges for detection. To address these issues, a model based on edge enhancement and multi-scale feature fusion for ship detection in remote sensing images was proposed. Firstly, a high-frequency feature enhancement module was designed to improve the ability of the model to capture fine details. Furthermore, an edge-guided multi-scale feature fusion method was proposed to mitigate the loss of edge information on low-level during propagation. Finally, a lightweight oriented detection head was constructed to reduce the params of the model Experimental results show that the improved model improves 3.6 and 2.1 percentage points of mAP50 on the ShipRSImageNet dataset and the HRSC2016 Dataset, compared to the YOLO11-obb model, effectively improves the accuracy of ship detection in remote sensing images.
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