Deep faster R-CNN-based automated detection and localization of multiple types of damage

Abstract

The primary method of structural health monitoring is human-based visual inspection, which—despite its limitations of consistency and accessibility—can warn about changes in a bridge’s condition. To improve the visual inspection of civil infrastructure and address these drawbacks of human-oriented inspection, computer vision-based techniques have been developed to detect structural damage in images. Most of these methods, however, detect only specific types of damage, such as cracks in concrete or steel. Another drawback is that the traditional convolutional neural network-based damage detection method is not able to provide the location of the detected damage. To provide quasi-realtime simultaneous detection and localization of multiple types of damage, a structural damage detection method based on Faster Regionbased Convolutional Neural Network (Faster R-CNN) is proposed. The original architecture of Faster R-CNN is modified, trained, validated, and tested for this study. The robustness of the trained Faster R-CNN is evaluated and demonstrated using seven new images taken of various structures.