Concrete Surface Crack Detection with Convolutional-based Deep Learning Models

TL;DR

This paper explores the use of fine-tuned convolutional neural networks like VGG19, ResNet50, Inception V3, and EfficientNetV2 for detecting surface cracks in buildings, addressing challenges like subtle features and lighting variations.

Contribution

It evaluates multiple pre-trained deep learning models for crack detection, providing a comparative analysis of their effectiveness in this application.

Findings

ResNet50 achieved the highest accuracy among models.

Transfer learning significantly improved detection performance.

Deep learning models outperformed traditional methods.

Abstract

Effective crack detection is pivotal for the structural health monitoring and inspection of buildings. This task presents a formidable challenge to computer vision techniques due to the inherently subtle nature of cracks, which often exhibit low-level features that can be easily confounded with background textures, foreign objects, or irregularities in construction. Furthermore, the presence of issues like non-uniform lighting and construction irregularities poses significant hurdles for autonomous crack detection during building inspection and monitoring. Convolutional neural networks (CNNs) have emerged as a promising framework for crack detection, offering high levels of accuracy and precision. Additionally, the ability to adapt pre-trained networks through transfer learning provides a valuable tool for users, eliminating the need for an in-depth understanding of algorithm intricacies. Nevertheless, it is imperative to acknowledge the limitations and considerations when deploying CNNs, particularly in contexts where the outcomes carry immense significance, such as crack detection in buildings. In this paper, our approach to surface crack detection involves the utilization of various deep-learning models. Specifically, we employ fine-tuning techniques on pre-trained deep learning architectures: VGG19, ResNet50, Inception V3, and EfficientNetV2. These models are chosen for their established performance and versatility in image analysis tasks. We compare deep learning models using precision, recall, and F1 scores.