EBDet: Ellipse Feature Encode and Balanced Decoupling Label Assignment for Arbitrary-Oriented Ship Detection in Optical Remote Sensing Images
Year: 2025
Type: article
Abstract: Arbitrary-oriented ship detection is a challenging task in optical remote sensing images (ORSIs). Existing methods mainly focus on improving model structures but seldom consider ship characteristics, such as shape and multiscale distribution. This results in prior ship-related information not being effectively utilized. To address these issues, a ship detector based on ellipse feature encoding (EFE) and balanced decoupling label assignment strategy (EBDet) is proposed, aiming to effectively integrate ship features during the crucial detection process. Specifically, the EFE strategy incorporates ship shape to represent the oriented bounding boxes (OBB) with seven parameters, and addresses the boundary discontinuity problem by resolving the contradiction between angular discreteness and network continuity. The ellipse balanced sampling (EBS) strategy fits the size and shape of ships to achieve a balance in the number of positive samples between ships of different scales. The dynamic decoupling label assignment (DDLA) strategy adaptively evaluates the sample space sensitivity, and mitigates the feature misalignment problem by decoupling positive samples in classification and regression. Extensive experiments demonstrate the effectiveness and advancement of our EBDet. Specifically, on the HRSC2016 dataset, EBDet achieves an mAP of 90.53% and 90.64% with ResNet-50 and ResNet-101 backbones, respectively. On the DIOR-Ship dataset, EBDet achieves mAP of 86.69% .
Authors: Tianqi Zhao, Yunhan Sun, Jin Qian, Zheng Li, Yunxiao Gao, Zhikang Zhao, Yongcheng Wang
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,800
OpenAlex ID: W4412375171