Automation in Construction, 173, 106099
Environment|구충완
Deep learning-based automated method for enhancing excavator activity recognition in far-field construction site surveillance videos
2025International Journals
Research AreaEnvironment
Professor구충완
AuthorsShin, Y., Seo, S., and Koo, C. (Corresponding Author)
Publication Year2025
Journal (Volume, Issue, Pages)Automation in Construction, 173, 106099
AbstractVision-based classifiers, highly sensitive to camera placement, face significant challenges under far-field conditions at construction sites. To address these challenges, this paper proposes a deep learning-based method for enhancing excavator activity recognition using a 3D Residual Neural Network (3D ResNet) classifier with transfer learning. Machine learning-based SHapley Additive exPlanations (SHAP) analysis was employed to evaluate classifier performance across varying camera placements, focusing on distance, height, and angle. Additionally, an image preprocessing method for object enlargement and clarity enhancement was introduced to improve accuracy. Key findings include: (i) optimal weighted F1-score of 0.866 achieved with camera placement at 20 m distance, 6 m height, and 45◦ angle; (ii) SHAP analysis identifying distance as the most critical factor; (iii) weighted F1-score of 0.818 obtained with real-world far-field video after applying the proposed image preprocessing. The proposed method demonstrates potential for enhancing productivity and carbon emissions management through precise excavator activity monitoring.
Computer Vision3D ResNetTransfer LearningFar-Field Construction SiteSurveillance VideosSynthetic VideoImage Preprocessing MethodActivity Recognition