Work

From Graph Synchronization to Policy Learning: Angle-Synchronized Graph and Bilevel Policy Network for Remote Sensing Object Detection

Year: 2025

Type: article

Abstract: Detection of rotating targets in complex remote sensing scenarios often suffers from angular inconsistencies and boundary jitter, especially for small-to-medium objects with rapid pose changes or indistinct boundaries in dense environments. To address this, we propose ASBPNet, a unified framework coupling geometric alignment with policy adaptation. It features the following: (1) Angle-Synchronized Graph (ASG), which injects angle–alignment relationships and residual-based boundary refinement to improve rotational consistency and reduce boundary errors for small objects; (2) Bilevel Policy Optimization (BPO), which unifies control over rotation enhancement, sample allocation, block scanning, and rotational NMS for cross-stage policy coordination and improved recall. Together, ASG and BPO form a tightly coupled pipeline in which geometric alignment directly reinforces policy optimization, yielding mutually enhanced rotation robustness, boundary stability, and detection recall across densely distributed remote sensing scenes. We conducted systematic evaluations on datasets including DIOR-R, HRSC2016, and DOTAv1.0: compared to baselines, overall accuracy achieved significant improvement on DIOR-R, with performance reaching 98.2% on HRSC2016. Simultaneously, enhanced robustness and boundary stability were demonstrated in complex backgrounds and dense small-object scenarios, validating the synergistic value of geometric alignment and policy adaptation.

Source: Remote Sensing

DOI: 10.3390/rs17244029

Authors: Jie Yan, Jialang Liu, Lixing Tang, Xiaoxiang Wang, Yanming Guo

Institutions: Beijing University of Posts and Telecommunications, National University of Defense Technology, Central South University of Forestry and Technology

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funders: Young Scientists Fund, Training Program for Excellent Young Innovators of Changsha