Developments in the Built Environment, 17, 100383
Environment|구충완
Precast concrete project image dataset for deep learning object detection
2024International Journals
Research AreaEnvironment
Professor구충완
AuthorsJang, J.Y., Jeong, E.B., Park, S.H., Koo, C. and Kim, T.W
Publication Year2024
Journal (Volume, Issue, Pages)Developments in the Built Environment, 17, 100383
AbstractComputer vision technology in precast concrete (PC) projects has the potential to enhance site management but faces challenges due to the lack of specialized datasets for this field. This study developed a PC components dataset for object detection (PCCODA) in four steps: component range selection, image collection, image preprocessing, and labeling. The performance of PCCODA was assessed using multiple algorithms, including You Only Look Once (YOLO), YOLO X, faster region-based convolutional neural networks (Faster R-CNN), and Double Head R-CNN. The dataset resulted in average accuracies of 0.88-0.97, average robustness of 0.84-0.96, and average detection speed of 3.64-23.3 frames per second. This performance fulfills the level required by most studies on construction automation. The applicability of the dataset was tested at a real site. This study contributes to the off-site construction project management theory and enhances productivity in PC projects by automating object detection-related tasks.
Off -Site ConstructionPrecast Concrete DatasetObject DetectionObject ClassificationComputer Vision
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