This study presents the Construction and Demolition Waste Object Detection Dataset (CODD), a benchmark dataset specifically curated for the training of object detection models and the full-scale implementation of automated sorting of Construction and Demolition Waste (CDW). The CODD encompasses a comprehensive range of CDW scenarios, capturing a diverse array of debris and waste materials frequently encountered in real-world construction and demolition sites. A noteworthy feature of the presented study is the ongoing collaborative nature of the dataset, which invites contributions from the scientific community, ensuring its perpetual improvement and adaptability to emerging research and practical requirements. Building upon the benchmark dataset, an advanced object detection model based on the latest bounding box and instance segmentation YOLOV8 architecture is developed to establish a baseline performance for future comparisons. The CODD benchmark dataset, along with the baseline model, provides a reliable reference for comprehensive comparisons and objective assessments of future models, contributing to progressive advancements and collaborative research in the field.
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