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<p>Efficient management of construction and demolition waste (CDW) is essential for enhancing resource recovery. The lack of publicly available, high-quality datasets for waste recognition limits the development and adoption of automated waste handling solutions. To facilitate data sharing and reuse, this study introduces ‘CDW-Seg’, a benchmark dataset for class-wise segmentation of CDW. The dataset comprises high-resolution images captured at authentic construction sites, featuring skip bins filled with a diverse mixture of CDW materials in-the-wild. It includes 5,413 manually annotated objects across ten categories: concrete, fill dirt, timber, hard plastic, soft plastic, steel, fabric, cardboard, plasterboard, and the skip bin, representing a total of 2,492,021,189 pixels. Each object was meticulously annotated through semantic segmentation, providing reliable ground-truth labels. To demonstrate the applicability of the dataset, an adapter-based fine-tuning approach was implemented using a hierarchical Vision Transformer, ensuring computational efficiency suitable for deployment in automated waste handling scenarios. The CDW-Seg has been made publicly accessible to promote data sharing, facilitate further research, and support the development of automated solutions for resource recovery.</p>

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A benchmark dataset for class-wise segmentation of construction and demolition waste in cluttered environments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月28日

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  • Diani Sirimewan,
  • Sanuwani Dayarathna,
  • Sudharshan Raman,
  • Yu Bai,
  • Mehrdad Arashpour

摘要

Efficient management of construction and demolition waste (CDW) is essential for enhancing resource recovery. The lack of publicly available, high-quality datasets for waste recognition limits the development and adoption of automated waste handling solutions. To facilitate data sharing and reuse, this study introduces ‘CDW-Seg’, a benchmark dataset for class-wise segmentation of CDW. The dataset comprises high-resolution images captured at authentic construction sites, featuring skip bins filled with a diverse mixture of CDW materials in-the-wild. It includes 5,413 manually annotated objects across ten categories: concrete, fill dirt, timber, hard plastic, soft plastic, steel, fabric, cardboard, plasterboard, and the skip bin, representing a total of 2,492,021,189 pixels. Each object was meticulously annotated through semantic segmentation, providing reliable ground-truth labels. To demonstrate the applicability of the dataset, an adapter-based fine-tuning approach was implemented using a hierarchical Vision Transformer, ensuring computational efficiency suitable for deployment in automated waste handling scenarios. The CDW-Seg has been made publicly accessible to promote data sharing, facilitate further research, and support the development of automated solutions for resource recovery.