Work

Change detection network for construction housekeeping using feature fusion and large vision models

Year: 2025

Type: article

Abstract: Although poor housekeeping leads to construction accidents, there is limited technological research on it. Existing methods for detecting poor housekeeping face many challenges, including limited explanations, lack of locating of poor housekeeping and annotated datasets. To address these challenges, this paper proposes the Housekeeping Change Detection Network (HCDN), integrating a feature fusion module and a large vision model. This paper introduces the approach to establish a change detection dataset (Housekeeping-CCD) focused on construction housekeeping, along with a housekeeping segmentation dataset. Experimental results of our Housekeeping-CCD dataset demonstrate that HCDN outperforms existing state-of-the-art (SOTA) methods, achieving average accuracy (89.32 %), mean IoU (76.97 %), and mean F-score (86.67 %). The contributions include significant performance improvements compared to existing methods, providing an effective tool for enhancing construction housekeeping and safety. • Proposed Housekeeping Change Detection Network (HCDN). • HCDN integrates Large Vision Model (LVM) and effective features fusion. • HCDN achieves superior performance than existing state-of-the-art methods. • Housekeeping detection system shows the potential in practical construction sites.

Source: Automation in Construction

DOI: 10.1016/j.autcon.2025.106038

Authors: Kailai Sun, Zherui Shao, Yang Miang Goh, Jing Tian, Vincent J.L. Gan

Institutions: National University of Singapore, Singapore-MIT Alliance for Research and Technology, Massachusetts Institute of Technology

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $5,490