Detection and Classification of Road Damage Based on Image Morphology and K-NN Method (K Nearest Neighbour)
Jennie Kusumaningrum1, Sarifuddin Madenda2, Karmilasari3, Nahdalina4

1Jennie Kusumaningrum, Department of Civil Engineering, Gunadarma University, Jakarta, Indonesia. 
2Sarifuddin Madenda, Department of Information Technology, Gunadarma University, Jakarta, Indonesia.
3Karmilasari*, Department of Information Technology, Gunadarma University, Jakarta, Indonesia.
4Nahdalina, Department of Civil Engineering, Gunadarma University, Jakarta, Indonesia.
Manuscript received on 25 April 2022. | Revised Manuscript received on 08 May 2022. | Manuscript published on 30 June 2022. | PP: 86-90 | Volume-11 Issue-5, June 2022. | Retrieval Number: 100.1/ijeat.E35430611522 | DOI: 10.35940/ijeat.E3543.0611522
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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: Crack, Alligator Crack, Potholes, Image Morphology, K-Nn Method
Scope of the Article: Classification