Vision-based volumetric estimation of localized construction and demolition waste

• Published in: Waste management (Elmsford) Vol. 206; p. 115046
• Main Authors: Jaiswal, Ashwani, Jha, Kunal, Bugalia, Nikhil, Ha, Quang Phuc
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.09.2025
• Subjects: Construction and demolition waste; Deep learning; Hybrid segmentation; Point cloud; Quantification; Reverse supply chain; Deep learning; Construction and demolition waste; Reverse supply chain; Point cloud; Quantification; Hybrid segmentation
• ISSN: 0956-053X; 1879-2456; 1879-2456
• DOI: 10.1016/j.wasman.2025.115046

[Display omitted] •Automated framework for localized CDW stockpile volume estimation.•184 point cloud dataset includes lab and field CDW and construction material scans.•Rule-based algorithm for ground plane identification using RANSAC.•ResNet-50 based multi-view classification model for accurate CDW classification.•Field-tested for on-field deployment with promising results. Accurate estimation of the quantity of localized construction and demolition waste (CDW) is critical for optimizing the upstream operations of the CDW’s reverse supply chain (RSC). However, existing studies extensively focus on downstream RSC operations with approaches that quantify large-scale material stockpiles through semi-automated workflows relying on expensive, non-portable devices. These approaches are impractical for upstream operations such as quantifying small-scale, localized CDW stockpiles scattered around urban environments, requiring frequent estimations. In contrast, this study proposes a novel vision-based framework that enables automated, fast, and accurate volume estimation of small-scale localized CDW using a consumer-grade imaging device. The framework incorporates a hybrid segmentation technique involving a ground plane identification process through a novel rule-based modification to the Random Sample Consensus (RANSAC) algorithm, followed by a clustering process. A new Multi-View Classification Model (MVCM) based on ResNet-50 architecture is also developed to recognize CDW clusters. A Delaunay triangulation-based approach estimates the volume of recognized CDW clusters. The framework is developed and validated using one of the most extensive datasets comprising 184 scans from the laboratory and the field environment. The MVCM achieved a high F1 score of 0.97 for identifying CDW using 3500 images. The framework demonstrates high accuracy for volume estimation, achieving an absolute percentage error (APE) of 8.97% compared to manual measurements. The overall process achieves an end-to-end processing time of 11 min, underscoring its efficiency and suitability for field deployment. The proposed framework is of significant practical value for localized CDW quantification and decision-making in upstream RSC operations.