Publication

Abstract : The construction of a building involves tremendous investments of time, money, and emotion. Therefore, every stakeholder involved in the process starting from construction companies to the tenants wants to make sure that a structure is built well and that it can serve its purpose without any safety hazards. While the majority of factors concerning a building’s safety are evaluated manually, there are factors like detecting visible structural damage that might incur a severe investment of time via manual inspection. Therefore, the need of the hour is to engineer automated systems that with the help of computer vision techniques will detect visually discernible defects in buildings. The paper proposes two approaches, namely digital image processing-based and deep learning-based that deal with creating surface crack inspection systems and attempt to showcase their performances in perspective by comparing their results across four different types of surface crack image datasets.