Structural Crack Detection and Classification using Deep Convolutional Neural Network

Abstract

Cracks are indicators that affect the stability and integrity of infrastructures. Fast, reliable, and cost-effective crack detection methods are required to overcome the shortcomings of traditional approaches. This paper works on a transfer learning approach based on the deep convolutional neural network model VGG19 to detect cracks. Further, the proposed method is based on an improved VGG-19 model. The experiment is carried out on the SDNET2018 annotated images dataset. The dataset comprises of total 15k images, training set consists of 5000 cracked and 5000 un-cracked images of walls, pavements, and bridges. The experimental results on the proposed model provide 91.8% accuracy in detecting cracks on the testing set. The paper concluded that fine-tuning of the VGG19 (Visual Geometry Group) model accomplish satisfactory results in detecting cracks on images of multiple infrastructures.

ROAD: Robotics-Assisted Onsite Data Collection and Deep Learning Enabled Robotic Vision System for Identification of Cracks on Diverse Surfaces

“…Automated crack detection can be used in various applications, such as building and infrastructure inspection, bridge inspection, and industrial equipment maintenance. Automated crack detection methods are essential for improving structural health monitoring and assessment and preventing potential safety hazards [1].…”