CSM-DETR: Construction Site Monitoring via Mamba-Enhanced Detection Transformer for UAV Aerial Imagery

Abstract Unmanned Aerial Vehicles (UAVs) offer significant advantages for construction site monitoring through flexible deployment and high-resolution imagery. However, existing vision-based detection methods face significant challenges including extreme scale variations, dense object distributions, complex backgrounds, and real-time processing constraints. To address these limitations, we propose CSM-DETR, a novel detection transformer specifically designed for UAV-based construction monitoring. Our framework adopts the MobileMamba as backbone to achieve linear computational complexity $\mathcal{O}(n)$ while capturing long-range spatial dependencies, and incorporates the Hierarchical Local-Aware Fusion (HLAF) mechanism for adaptive multi-scale feature aggregation. Furthermore, we propose three key innovations: (1) a Dual-Attention Spatial Integration (DASI) module enhancing multi-scale spatial feature representation through parallel local and global attention streams; (2) a Cross-Scale Deformable Fusion (CSDF) module enabling flexible cross-scale feature interaction through deformable sampling; and (3) a Scale-Aware Composite Loss (SAC Loss) providing scale-aware supervision for challenging small objects. We construct a comprehensive benchmark dataset named UAV-CSM47, containing 15,860 high-resolution aerial images with 47 construction-related object categories. Extensive experiments demonstrate that CSM-DETR achieves state-of-the-art performance with 91.8\% mAP@0.5 and 73.6\% mAP@0.5:0.95, outperforming YOLOv13-L by 3.3 percentage points and Co-DETR by 2.7 percentage points while maintaining real-time inference at 38 FPS. Ablation studies validate each component's effectiveness, and cross-domain evaluation confirms strong generalization capability. The proposed system provides a practical solution for automated construction site monitoring with broad applications in safety supervision, progress tracking, and resource management.