Hongru Xiao , Bin Yang , Yujie Lu , Wenshuo Chen , Songning Lai , Biaoli Gao
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引用次数: 0
Abstract
Accurate and real-time object detection in complex construction scenes from multiple viewpoints plays a crucial role in effective project management. However, this task remains limited by boundary information sharing and scene sensitivity inherent in deep features. To investigate the deep features in construction scenes and analyze method performance, SODA and VisDrone datasets, mean Average Precision (mAP) series metrics, visual inspection, Grad-CAM, and ablation studies are utilized. This paper proposes a lightweight Transformer-based detection framework named Complex Construction Scenes Transformer (CCS-TR), which integrates with a Scale-Isolate Fusion Attention (SIFA) mechanism and an Instructive Contrastive Learning (ICL) strategy. Evaluation results demonstrate that CCS-TR achieves a 5.1 %–8.8 % improvement in detection accuracy while maintaining lower computational costs, making it suitable for real-time on-site detection. Future work will address detection in interacting complex scenes and develop multi-modal collaboration strategies for extreme lighting.
期刊介绍:
Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities.
The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.