Unsupervised method for detection of high severity distresses on asphalt pavements

• Published in: 2017 IEEE 14th International Scientific Conference on Informatics pp. 45 - 50
• Main Authors: Buza, Emir, Akagic, Amila, Omanovic, Samir, Hasic, Haris
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2017
• Subjects: Asphalt; Computer Vision; Image Clustering; Image processing; Image segmentation; Inspection; Linear regression; Pothole detection; Shape; Unsupervised Classification; Videos
• ISBN: 153860888X; 9781538608883
• DOI: 10.1109/INFORMATICS.2017.8327220

Efficient detection of distresses on asphalt pavements has a great impact on safe driving, thus it has been very active research subject in recent years. High severity level distresses, such as potholes, are the most severe threat to safe driving, hence timely detecting and repairing potholes is crucial in ensuring safety and quality of driving. Existing methods often require sophisticated equipment and algorithms with high-computational pre-processing steps for analysis of substantial amount of existing data (images or videos). In this paper, a new unsupervised method for detection of high severity distresses on asphalt pavements was proposed. The method was tested on highly unstructured image data set captured from different cameras and angles, with different irregular shapes and number of potholes to demonstrate its capability. Results indicated that the method can be used for rough detection and estimation of damaged pavements.