Fuzzy Extensions of Isolation Forests for Road Anomaly Detection

Abstract

In the presented paper the authors are showing the usage of fuzzy extensions of isolations forests for detecting road anomalies like potholes. Using the data acquired by the accelerometer in the smartphone and the proper smartphone application, the vibrations while driving over road were analyzed using multiple variants of extended isolation forests - n-ary (NIF), with fuzzy membership function (MIF), with k-means clustering (KIF), with two fuzzy clusters incorporated (CIF) or two fuzzy clusters and the distance to the cluster center (prototype) utilized (C2DIF). The presented research shows that in comparison to the state-of-the-art methods previously discussed by the authors, the accuracy and false positive rate have improved, while the sensitivity has been improved to reach 100%.