Evolution and challenges of computer vision and deep learning technologies for analysing mixed construction and demolition waste

• URL: http://arxiv.org/abs/2409.13112v1
• Date: Thu, 19 Sep 2024 22:38:26 GMT
• Title: Evolution and challenges of computer vision and deep learning technologies for analysing mixed construction and demolition waste
• Authors: Adrian Langley, Matthew Lonergan, Tao Huang, Mostafa Rahimi Azghadi,
• Abstract summary: This paper explores the challenges and opportunities in developing an advanced automated mixed C&DW management system. We review various C&DW analysis techniques, concluding that DL-based visual methods are the optimal solution. We also discuss C&DW datasets, their curation, and innovative methods for their creation.
• Score: 5.681772950719116
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Improving the automatic and timely recognition of construction and demolition waste (C&DW) composition is crucial for enhancing business returns, economic outcomes, and sustainability. Technologies like computer vision, artificial intelligence (AI), robotics, and internet of things (IoT) are increasingly integrated into waste processing to achieve these goals. While deep learning (DL) models show promise in recognising homogeneous C&DW piles, few studies assess their performance with mixed, highly contaminated material in commercial settings. Drawing on extensive experience at a C&DW materials recovery facility (MRF) in Sydney, Australia, we explore the challenges and opportunities in developing an advanced automated mixed C&DW management system. We begin with an overview of the evolution of waste management in the construction industry, highlighting its environmental, economic, and societal impacts. We review various C&DW analysis techniques, concluding that DL-based visual methods are the optimal solution. Additionally, we examine the progression of sensor and camera technologies for C&DW analysis as well as the evolution of DL algorithms focused on object detection and material segmentation. We also discuss C&DW datasets, their curation, and innovative methods for their creation. Finally, we share insights on C&DW visual analysis, addressing technical and commercial challenges, research trends, and future directions for mixed C&DW analysis. This paper aims to improve the efficiency of C&DW management by providing valuable insights for ongoing and future research and development efforts in this critical sector.

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