Building Crack Detection Using CNN and Class Activation Mapping

As the population increases, further structures are being erected to meet the demands of the populace. Nonetheless, this surge in development presents the challenge of guaranteeing that each edifice fulfills the aspirations of its inhabitants. Identifying and rectifying damage or defects in structures can be challenging, particularly when conventional techniques typically necessitate comprehensive examinations to pinpoint issues. To address this difficulty, we are utilizing advanced technology such as Deep Learning models, particularly Convolutional Neural Networks (CNNs), along with approaches like Class Activation Mapping (CAM). These models utilize extensive datasets to learn and recognize patterns, enabling them to precisely identify specific forms of damage or defects in structures. We intend to forecast probable damage rates in advance through the application of these Deep Learning approaches. This proactive strategy enables us to avert catastrophic incidents by recognizing and resolving problems prior to their escalation. The great precision of these models ensures that buildings are safe and fulfill tenant expectations, hence enhancing the safety and reliability of structures within our communities. Our model attained an accuracy of 88%.