Harnessing Deep Learning Techniques for Enhanced Detection and Classification of Cracks in Pavement Imagery

Abstract

With the increased demand for infrastructure maintenance and the evolving capabilities of deep learning, there is a pressing need to apply advanced techniques for efficient and accurate detection of structural anomalies. This paper delves into the application of Convolutional Neural Networks (CNNs) and transfer learning to address the problem of pavement crack classification using images. Three distinctive models were explored: a 4-layer CNN, a 2-layer CNN, and a VGG-based transfer learning model. Through comprehensive experimentation, we demonstrate the efficacy of these models in accurately classifying Pavement cracks from a curated dataset. Our findings indicate that while each model possesses its unique strengths, the VGG-based transfer learning model exhibits superior performance in terms of precision and recall. This research not only contributes to the growing body of knowledge in infrastructure maintenance using deep learning but also provides practical insights for professionals aiming to employ automated systems for Pavement inspection.