Road damage severity estimation using isolation forest and variational autoencoder

Abstract

The durability and safety of road infrastructure are essential for economic activity and public safety. However, the traditional manual inspection of road surfaces for damage is often inefficient, costly, and susceptible to human error, making automated solutions highly desirable. This research explores the potential of unsupervised anomaly detection techniques, namely Variational Autoencoders (VAEs) and Isolation Forests (iForest), for automating road damage detection and severity estimation. By training these models solely on undamaged road images, this study leverages latent space representations from VAEs and anomaly scoring from iForest to detect deviations indicative of road damage. Testing was conducted on a diverse dataset of road images, allowing the models to identify damage and classify its severity into three categories: low, medium, and high. The study also compares these results with those of the RT-DSAFDet (Dynamic Scale-Aware Fusion Detection) model. While iForest and VAE achieved high recall rates and performed well in severity classification, RT-DSAFDet demonstrated superior adaptability to complex road scenarios and lighting conditions, making it well-suited for real-time applications. ii