Structural Crack Detection and Recognition Based on Deep Learning
Source abstract
Abstract
The detection and recognition of surface cracks are of great significance for structural safety. This paper is based on a deep-learning methodology to detect and recognize structural cracks. First, a training dataset of the model is built. Then, three neural networks, AlexNet, VGGNet13, and ResNet18, are employed to recognize and classify crack images. The tests indicate that the ResNet18 model generates the most satisfactory results. It is also found that the trained YOLOv3 model detects the crack area with satisfactory accuracy. This study also confirms that the proposed deep learning as a novel technology has the potential to be an efficient and robust tool for crack detection and recognition to replace traditional methods.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · Structural Health Monitoring Techniques · Concrete Corrosion and Durability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Naval Architecture, Dalian University of Technology, Dalian 116024, China
State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024, China
References (30)
Show source-provided references
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