Automated Vision-Based Detection of Cracks on Concrete Surfaces Using a Deep Learning Technique

TLDR

Computer‑vision crack‑detection methods exist for large‑scale inspection, but they have not supplanted visual inspection because they were developed under near‑ideal conditions rather than on‑site environments. The study proposes an automated on‑site crack‑detection technique using convolutional neural networks. The authors trained AlexNet on a five‑class dataset of crack, intact, similar patterns, and plants, employed a softmax‑based probability map with threshold tuning, and evaluated the model on field images and UAV video frames. Evaluation shows the method is highly adoptable for on‑site crack inspection.

Abstract

At present, a number of computer vision-based crack detection techniques have been developed to efficiently inspect and manage a large number of structures. However, these techniques have not replaced visual inspection, as they have been developed under near-ideal conditions and not in an on-site environment. This article proposes an automated detection technique for crack morphology on concrete surface under an on-site environment based on convolutional neural networks (CNNs). A well-known CNN, AlexNet is trained for crack detection with images scraped from the Internet. The training set is divided into five classes involving cracks, intact surfaces, two types of similar patterns of cracks, and plants. A comparative study evaluates the successfulness of the detailed surface categorization. A probability map is developed using a softmax layer value to add robustness to sliding window detection and a parametric study was carried out to determine its threshold. The applicability of the proposed method is evaluated on images taken from the field and real-time video frames taken using an unmanned aerial vehicle. The evaluation results confirm the high adoptability of the proposed method for crack inspection in an on-site environment.