Overview of Building Disease Detection Based on Convolutional Neural Network

Convolutional Neural Networks (CNNs) leverage their innovative capability for efficient imaging functions for large-scale applications in the field of building health control and intelligent buildings. By recognizing damage, CNN overcomes the obstacles of classic error recognition algorithms, such as cracks and cracks, through learning. General models such as Mask R-CNN and YOLO have more than 90 percent classification accuracy for multi-component recognition. In terms of smart building, CNN integrates an Edge Computing program that provides real-time building quality monitoring (concrete fault detection), security alert (personal computer recognition), and automatic progress management. Technology development focuses on three main areas: simple architecture (MobileNet/deep separation convolution) to reduce availability costs; The combination of functions greatly improves adaptability to complex scenarios. Optimizing the attention mechanism improves the accuracy of detecting minor errors. Despite challenges such as extreme environmental sustainability and the ability to spread small samples, CNN technology is driving the shift in building inspection from empirical assessment to information-based decision-making and providing critical support for the smart use and maintenance of infrastructure.