CRACK VISION: SOPHISTICATED CONCRETE CRACK IDENTIFICATION THROUGH TRANSFER LEARNING AND DEEP LEARNING

Abstract

Crack Vision is a deep learning-powered application designed to detect and classify cracks in concrete surfaces with exceptional accuracy. Unlike traditional manual inspection methods, which are time-consuming and prone to human error, Crack Vision leverages state-of-the-art convolutional neural networks and transfer learning techniques—enhanced with alternative architectures such as EfficientNetB3—to deliver reliable, realtime predictions. The system has been trained on the METU concrete crack dataset, containing balanced sets of cracked and noncracked surface images, enabling robust binary classification. Integrated into a userfriendly Flask web interface, Crack Vision allows users to easily upload images, preview them, and instantly receive classification results along with confidence scores. Additional visualization tools, including accuracy/loss charts and confusion matrices, provide transparency into model performance. This scalable solution offers a practical tool for engineers, inspectors, and infrastructure maintenance teams, enabling faster, more consistent assessments and contributing to improved structural safety and long-term durability in civil engineering projects. Index Terms – Concrete crack detection, deep learning, convolutional neural network (CNN), EfficientNetB3, transfer learning,computer vision, structural health monitoring, image classification, Flask web application, infrastructure inspection, civil engineering, crack classification.