Real-Time Safety-Critical Object Detection in Large Open Construction Sites Using a Scale-Gated Edge Detection Transformer

Wide-area visual monitoring of construction sites is constrained by the reliable detection of safety-critical targets that appear small, low-resolution, and weakly textured under elevated or distant camera views. To address this problem, this study proposes Scale-Gated Edge Detection Transformer (SGE-DETR), a safety-oriented end-to-end detector for large open construction scenes. The framework incorporates scale-aware residual edge modulation to preserve weak contours and local structures, density-guided context-adaptive fusion to balance multi-level features according to contextual and edge-density responses, and spatial gated reparameterized feature refinement to suppress redundant background textures. Experiments were conducted on SODA and STWD using COCO-style scale-sensitive metrics and efficiency indicators. On SODA, SGE-DETR achieved AP50, APS, APM, and APL values of 0.8748, 0.2157, 0.4577, and 0.6013, respectively, with 32.5 GFLOPs, 14.5 M parameters, and 83.4 FPS. On STWD, it obtained the highest AP50, APS, APM, and APL among the compared methods, reaching 0.7936, 0.8132, 0.8594, and 0.9253, respectively. Ablation results further showed that the full model improved mAP50 and mAP50–95 over RT-DETR-r18 by 4.15 and 2.93 percentage points while reducing computational complexity. These results indicate that SGE-DETR improves safety-oriented small-object detection and multi-scale robustness while retaining a relatively low parameter count.