Improvement of Concrete Crack Segmentation Performance Using Stacking Ensemble Learning
نویسندگان
چکیده
Signs of functional loss due to the deterioration structures are primarily identified from cracks occurring on surface structures, and continuous monitoring structural is essential for socially important structures. Recently, many crack technologies have been developed with development deep-learning artificial intelligence (AI). In this study, stacking ensemble learning was applied predict more precisely. A semantic segmentation model used detection using a deep AI model. We studied crack-detection performance by training UNet, DeepLabV3, DeepLabV3+, DANet, FCN-8s. Owing unsuitable FCN-8s, conducted remaining four models. Individual models yielded an intersection over union (IoU) score ranging approximately 0.4 0.6 test dataset. However, when metamodel completed used, IoU 0.74, indicating high-performance improvement. total 1235 images acquired drones sea bridge, showed 0.5 or higher 64.4% images.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2023
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app13042367