Sirimewan, Diani Chamathya (2025) Optimising construction and demolition waste handling using computer vision techniques. PhD thesis, Monash University, Australia.
Abstract
This thesis develops and implements advanced computer vision techniques for efficient segmentation and recognition of construction and demolition waste (CDW), addressing the limitations of manual waste handling at material recovery facilities. A realistic CDW dataset from construction sites was curated, supporting robust evaluation of state-of-the-art deep learning models. To reduce annotation dependence, a semi-supervised adversarial network (DuoSeg++), and a user-interactive system, (PromSeg-Waste), were introduced, enhancing the segmentation performance. Further, the WasteXtract model adapted large-scale vision foundation models for efficient deployment in resource-constrained environments. The release of the CDW-Seg dataset provides comprehensive benchmarking, improving CDW management efficiency, accuracy, and sustainability.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Arashpour, Mehrdad and Bai, Yu |
| Uncontrolled Keywords: | construction and demolition waste; waste recognition; waste segmentation; waste sorting; waste recycling; computer vision; deep learning |
| Index terms: | interactive system, accuracy, efficiency, construction and demolition, dataset, deep learning, benchmarking, recycling, material recovery, computer vision, construction site, state of the art |
| Subjects: | waste management, artificial intelligence, data management, performance management, research dissemination and communication, sustainable design, work location, software systems, computer vision, performance measurement, professional development |
| Topics: | Information Management, Digital Applications, Quality Management, Site Management, Sustainability, Research Practice |
| Descriptive scope: | 2 PC |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here