Automatic detection of deteriorated inverted-T patching using 3D laser imaging system based on a true story indiana

Abstract

Deteriorated Inverted-T patching can lead to uneven settlement, dip, or ref lective transverse cracking on the asphalt overlay. This paper demonstrates a hybrid method to automatically detect deteriorated Inverted-T patching for an efficient maintenance schedule. First, hundreds of 2D/3D pavement images with deteriorated Inverted-T patching were manually identified and labelled from more than 400 miles of field data in Indiana. All data were collected through a high-speed 3D laser imaging system. Afterward, three deep learning architectures, including the Single Shot Detector network (SSD300), an advanced Region-based Convolutional Neural Network (Mask RCNN), and a fast and precise convolutional network (U-Net), were applied to develop artificial intelligence models to identify deteriorated Inverted-T patching from 3D images. The results indicate that the Mask R-CNN model can achieve good detection accuracy only on the prepared testing images. Further, a hybrid deep learning model was developed to combine International Roughness Index (IRI) values and the corresponding 3D images to detect deteriorated Inverted-T patching. The hybrid method was promising and significantly improved the efficiency of locating deteriorated Inverted-T patching from network screening.