Real-time assessment of surface cracks in concrete structures using integrated deep neural networks with autonomous unmanned aerial vehicle

Abstract

Ensuring the structural integrity of civil infrastructures is critical, given the potential hazards presented by cracks. Traditional manual inspections, though common, often face challenges related to accuracy, accessibility, safety, and cost-effectiveness. To address these issues, this study presents a new approach that integrates unmanned aerial vehicles (UAVs) with advanced computer vision techniques. A key feature of our approach is a system where a self-operating UAV is paired with a carefully crafted deep-learning design. This design takes advantage of both the Fast Region-based Convolutional Neural Network (FRCNN) and Residual Network (ResNet) models, allowing for real-time and accurate crack detection from ongoing video feeds. For uninterrupted real-time processing, our UAV is equipped with a data transmission module, sending live video to a computational platform enhanced with our advanced crack-spotting tool. After thorough training using a diverse dataset of 1000 images, our FRCNN-ResNet model was compared to other top models, achieving a precision rate of 93.3% and a quick 59.7ms inference time. Overall, this study offers a notable improvement in how we monitor civil structures, focusing on better safety and cost savings.