DMG-YOLO: Boosting Real-Time Small Object Detection in Remote Sensing with Lightweight Efficiency
By Qianyi Wang, Guoqiang Ren
Published on November 24, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on cs.CV updates on arXiv.org.
Summary\
Researchers propose DMG-YOLO, a novel lightweight real-time detector specifically engineered for small object detection in remote sensing imagery. This model tackles the inherent challenges of balancing detection accuracy with computational efficiency. Its architecture features a Dual-branch Feature Extraction (DFE) module in the backbone, leveraging both local features via depthwise separable convolutions and global context through a gated vision transformer. Additionally, a Multi-scale Feature Fusion (MFF) module utilizes dilated convolutions, and a Global and Local Aggregate Feature Pyramid Network (GLAFPN) enhances small object detection. Experimental results on datasets like VisDrone2019 and NWPU VHR-10 demonstrate DMG-YOLO's competitive performance across key metrics including mAP and model size.
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Why It Matters\
This research represents a significant stride in making sophisticated AI accessible and practical for critical remote sensing applications. The ability to perform "real-time" and "lightweight" small object detection is not merely an incremental improvement; it's a foundational enabler for numerous industries. Imagine drones autonomously identifying subtle signs of crop disease, rapidly assessing disaster damage, or monitoring illegal deforestation from satellite imagery - all instantaneously, without the need for massive computational infrastructure. For AI professionals, DMG-YOLO exemplifies the crucial trend towards optimizing model efficiency and deployability, moving beyond raw accuracy benchmarks to focus on "AI at the edge." This innovation will empower solution architects to build more responsive and scalable remote sensing systems, facilitate new capabilities in environmental monitoring, defense, urban planning, and precision agriculture. Furthermore, the hybrid architectural approach, combining CNNs for local features and vision transformers for global context, showcases intelligent model design that addresses the complex challenges of varying object scales and environments, setting a precedent for future efficient and powerful computer vision models.