A lightweight detector for real-time detection of remote sensing images

• URL: http://arxiv.org/abs/2511.17147v1
• Date: Fri, 21 Nov 2025 11:11:04 GMT
• Title: A lightweight detector for real-time detection of remote sensing images
• Authors: Qianyi Wang, Guoqiang Ren,
• Abstract summary: DMG-YOLO is a lightweight real-time detector tailored for small object detection in remote sensing images.<n>We introduce a Dual-branch Feature Extraction (DFE) module in the backbone, which partitions feature maps into two parallel branches.<n>In the neck, we introduce the Global and Local Aggregate Feature Pyramid Network (GLAFPN) to further boost small object detection.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Remote sensing imagery is widely used across various fields, yet real-time detection remains challenging due to the prevalence of small objects and the need to balance accuracy with efficiency. To address this, we propose DMG-YOLO, a lightweight real-time detector tailored for small object detection in remote sensing images. Specifically, we design a Dual-branch Feature Extraction (DFE) module in the backbone, which partitions feature maps into two parallel branches: one extracts local features via depthwise separable convolutions, and the other captures global context using a vision transformer with a gating mechanism. Additionally, a Multi-scale Feature Fusion (MFF) module with dilated convolutions enhances multi-scale integration while preserving fine details. In the neck, we introduce the Global and Local Aggregate Feature Pyramid Network (GLAFPN) to further boost small object detection through global-local feature fusion. Extensive experiments on the VisDrone2019 and NWPU VHR-10 datasets show that DMG-YOLO achieves competitive performance in terms of mAP, model size, and other key metrics.

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