Damaged ceiling detection and localization in large-span structures using convolutional neural networks

Abstract

To overcome the limitations of human-based visual onsite inspections, a vision-based method using deep learning with a convolutional neural network (CNN) is proposed to detect and localize the damaged ceiling of large-span structures. The designed CNN model is trained, validated, and tested using 1953 ceiling images, and a prediction accuracy of 86.22% is obtained. The results of a comparative study demonstrate that the saliency map method can accurately localize regions with damaged ceiling and demonstrate the outline shape of the damaged regions. The features visualization using a saliency map reveals that the CNN model is capable of recognizing the overall layout of the inside of a building through images of the intact part of the building and regions with damaged ceiling through images of damaged areas, although, the non-ceiling regions, particularly isolated regions with regular shapes, have a significant influence on the damage prediction probability. Non-ceiling regions and the area ratio are two important factors influencing the prediction accuracy of the CNN model. A statistical analysis indicates that a prediction accuracy of greater than 98% can be obtained in the case of no non-ceiling regions and an area ratio ranging from 20% to 30%. Therefore, photographic method is proposed for capturing ceiling images and improving the prediction accuracy of the CNN model.