Detection of Asphalt Pavement Damage using YOLOv11 Segmentation on SDI and IKP Methods in Road Condition Classification

Idy Safriadi; Leksmono Suryo Putranto; Said Basalim

doi:10.29138/ijebd.v9i4.3758

Idy Safriadi 1Civil Engineering Doctoral Program, Tarumanagara University - Jakarta, Indonesia
Leksmono Suryo Putranto Civil Engineering Doctoral Program, Tarumanagara University - Jakarta, Indonesia
Said Basalim Bachelor of Civil Engineering Study Program, Tanjungpura University Pontianak, Pontianak, Indonesia

DOI: https://doi.org/10.29138/ijebd.v9i4.3758

Keywords: Computer Vision, Surface Distress Index, Pavement Condition Index, Road Infrastructure Assessment

Abstract

Purpose: Assessing the condition of road infrastructure in Mempawah Hilir Regency which has a total road length of 48.20 KM using conventional methods is time-consuming and prone to subjective bias. This study proposes the digitization of road pavement condition evaluation through the development of a computer vision-based analytical system.

Design/methodology/approach: This system integrates the YOLO11 artificial intelligence model and the ByteTrack object detection algorithm to automatically extract road surface anomaly metrics from survey video recordings. The evaluation is quantified using the Surface Distress Index (SDI) and the Pavement Condition Index (PCI).

Findings: Case study test results show an SDI value of 137.50 (Mild Damage category, Rehabilitation recommendation) due to the severity of specific focal degradation, while the IKP extraction achieved a score of 71.41 (Good category, Periodic Maintenance recommendation) given that the macro-level damage area ratio was very minimal, at 0.56%. The integration of this technology has proven to accelerate the visual inspection process and provide a comprehensive comparative perspective to support precise and efficient decision-making regarding road infrastructure maintenance.

Paper type: Research Paper