苏州科技大学学报（自然科学版）2026，Vol.43Issue(2)：52-61,10.DOI:10.12084/j.issn.2096-3289.2026.02.007
基于HG-RTDETR的建筑工地小目标检测方法
HG-RTDETR for small object detection in construction sites
摘要
Abstract
To address the challenges of limited pixel information,unstable illumination,large-scale variations,and occlusion interference in small-object detection within construction sites,this paper proposes an improved Transformer-based detection model,HG-RTDETR.A Single-Head Gated feature extraction module(SHG)was constructed,employing a single-head attention mechanism to jointly model global context and local features,with a gating mechanism to optimize feature selection and enhance discrimination under occlusion.A Cross-Scale Channel and Context Feature Fusion(CS-CCFF)was designed,incorporating Reconfigurable-Field Attention Convolution(RFAConv)and Hierarchical Feature Integrator(HFI).RFA adaptively adjusts receptive fields for multi-scale fea-ture extraction while suppressing noise,and HFI integrates high-level semantics,mid-level details,and large-scale features via a feature pyramid architecture to establish cross-level feature interaction.In addition,the MPDIoU loss function was introduced during bounding-box regression to refine the alignment between predicted and ground-truth boxes,improving small-object localization accuracy and robustness.Experiments on the MOCS construction dataset demonstrate improvements of 2.8%in mAP50 and 1.8%in mAP50:95.关键词
小目标检测/RT-DETR/跨尺度特征融合/门控注意力机制Key words
small object detection/RT-DETR/cross-scale feature fusion/gated attention mechanism分类
建筑与水利引用本文复制引用
杨亚龙,崔昊宇,苏亮亮,陈永麟..基于HG-RTDETR的建筑工地小目标检测方法[J].苏州科技大学学报（自然科学版）,2026,43(2):52-61,10.基金项目
国家重点研发计划项目(2024YFC3808100) （2024YFC3808100）
安徽建筑大学科研项目(JZ202383 （）
JZ202372) （）
