Crack Detection in Building Through Deep Learning Feature Extraction and Machine Learning Approch

Buildings with cracks are extremely hazardous because they have the potential to cause destruction. Numerous occupants of structures such as houses and buildings are at risk when cracks appear. There are numerous techniques for identifying fractures in structures, including visual inspection, tool use, and expert inspection. The present study employed computer vision, a form of artificial intelligence, to detect cracks in buildings. The main objective of this research is to construct a prototype capable of real-time monitoring of cracks in building walls. This research makes use of a methodology that combines machine learning and deep learning. Machine learning is employed in the classification process, whereas deep learning is utilized for the extraction of features. This research employs MobileNetV2 as its deep learning architecture and K-NN, Naive Bayes, SVM, XGBoost, and Random Forest as its machine learning classifiers. Test results show that when dividing the 80:20 dataset, XGBoost algorithms can produce the highest accuracy, sensitivity, and specificity values of 99%. Tests in the real environment are performed by deploying Raspberry Pi. Test results show that the prototype can detect cracks inthe structure surfaceat a distance of 10 meters in a bright environment. The crack detection process is carried out in real time at an average speed of 42fps.