Detection and Classification of Road Damage Based on Image Morphology and K-NN Method (K Nearest Neighbour)
Open Access
- 30 June 2022
- journal article
- Published by Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP in International Journal of Engineering and Advanced Technology
- Vol. 11 (5), 86-90
- https://doi.org/10.35940/ijeat.e3543.0611522
Abstract
Road pavement is a supporting factor for national development, especially in the distribution of trade in goods and services as well as the movement of human mobility. Road maintenance needs to be done regularly so that the road is always in good condition, but the weather and road loads are the things that cause road damage. Road damage is generally categorized into cracks, alligator cracks and potholes. The purpose of this research is to utilize image processing to detect and classify the types of road damage. The steps involved include: image acquisition with a digital camera, conversion of RGB images into grayscale images, image normalization, selection of damage points, counting histogram bins, determining damage bins, calculating noise with image morphology (closing and opening) using a disk element structure of size 5, calculating radial vector and finally classifying road damage using the K-NN (K Nearest Neighbor) method with 3 classes and a K value of 11. The image from the classification results is then calculated the level of damage based on the category according to the SDI (Surface Distress Index) provisions, where the level of crack damage is seen from the width of the crack, the alligator crack is seen from the percentage of damaged area compared to the segment under review and the pathole is seen from many holes. The test used 597 images consisting of 95% training data and 5% test data, the results obtained that the accuracy of this research reached 83%.Keywords
This publication has 10 references indexed in Scilit:
- Effectiveness of Longsegment Contract Method on The Road Rehabilitation and Maintenance SystemJournal of Physics: Conference Series, 2021
- Automatic Pavement-Crack Detection and Segmentation Based on Steerable Matched Filtering and an Active Contour ModelJournal of Computing in Civil Engineering, 2017
- Recognition of asphalt pavement crack length using deep convolutional neural networksRoad Materials and Pavement Design, 2017
- Automated Detection of Multiple Pavement DefectsJournal of Computing in Civil Engineering, 2017
- Comparison of Supervised Classification Techniques for Vision-Based Pavement Crack DetectionTransportation Research Record: Journal of the Transportation Research Board, 2016
- Iterative Tensor Voting for Pavement Crack Extraction Using Mobile Laser Scanning DataIEEE Transactions on Geoscience and Remote Sensing, 2014
- Automatic Road Crack Detection and CharacterizationIEEE Transactions on Intelligent Transportation Systems, 2012
- Automatic Road Pavement Assessment with Image Processing: Review and ComparisonInternational Journal of Geophysics, 2011
- Adaptive Road Crack Detection System by Pavement ClassificationSensors, 2011
- Evaluating Pavement Cracks with Bidimensional Empirical Mode DecompositionEURASIP Journal on Advances in Signal Processing, 2008