Abstract
Monitoring the health of the structure is pivotal for maintenance and damage forecast as they are used for monitoring the performance of the structure and damage evaluation. The soundness of RCC structure is effected by the defects on its surface by providing a passage to the agents such as moisture, and acid-forming gases present in the atmosphere into the structure. To increase the service life of the structure, rehabilitation techniques will be used. For this purpose, defects must be detected. Though traditional methods of detection are accurate, the outcomes will depend on the knowledge and experience of inspectors. Because of this reason image processing techniques (IPTs) were introduced in SHM. In this work, a concrete defect classifier will be proposed, in line with guidelines for inspection, that can detect multiple unhealthy areas in concrete structures into a specific data type. The model that we are going to develop will be trained with 6806 images. The labeling process will be carried out using Roboflow software. Object detection models like YOLO-X, YOLO-v4, YOLO-v5, YOLO-v6, YOLO-v7 and YOLO-v8 were deployed on the labeled images and YOLO-v8 stood as optimal model based on mAP@.0.5, mAP@.0.5:0.95 i.e., 97.2% and 85.6% and can be used for the multi-classifier.
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Sai, V.V.S., Mohammad, A.A.K. (2023). Multi-classifier for Civil Infrastructure Damage Detection. In: Choudrie, J., Mahalle, P.N., Perumal, T., Joshi, A. (eds) IOT with Smart Systems. ICTIS 2023. Lecture Notes in Networks and Systems, vol 720. Springer, Singapore. https://doi.org/10.1007/978-981-99-3761-5_46
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