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Yuwono Marta Dinata
Universitas Ciputra Surabaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Intelligent Segmentation of Road Surface Damage Using Deep Learning Approaches Rudi Limantara; Arya Athaullah Rafi; Darren Wibisono; Citra Lestari; Satria Adi Nugraha; Yuwono Marta Dinata
Intelligent System and Computation Vol 8 No 1 (2026): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v8i1.488

Abstract

Road surface damage, including cracks and structural cavities, poses significant risks to vehicle safety and sustainable infrastructure management. Conventional road assessment remains largely manual, time-consuming, and subjective, creating a need for automated and scalable solutions. This study presents a deep learning framework for pixel-level pavement damage segmentation using an in-house dataset of 3,000 annotated road images collected under diverse real-world conditions. The framework comprises image preprocessing, model training, and standardized performance evaluation. Three established segmentation models, namely U-Net, DeepLabV3+, and YOLOv8-seg, were comparatively evaluated under consistent experimental settings using Intersection over Union (IoU), Dice coefficient, and inference speed. The results show that all three models can identify damaged road regions, although their performance varies according to architectural characteristics and damage patterns. U-Net achieved the highest IoU and Dice scores, demonstrating strong capability in preserving spatial information and delineating fine-grained damage, particularly thin cracks. DeepLabV3+ produced competitive results in segmenting larger and context-dependent damage areas through multi-scale feature extraction. YOLOv8-seg provided faster inference and greater potential for real-time applications, although its segmentation accuracy was lower than that of U-Net and DeepLabV3+. These findings demonstrate that model selection should consider the trade-off between segmentation accuracy and computational efficiency. U-Net was the most effective model for precise pixel-level road damage segmentation in this dataset. Future research will focus on broader datasets, additional architectures, and deployment-oriented optimization for intelligent road maintenance systems.
Co-Authors Aldillah Abubakar Alimmada Ammar Sharhanata Amadeus Michel Goein Andriani, Nathania Disa Ariesta Angga Candra Wardana Arya Athaullah Rafi Billy Daniel Messakh Christian Tanjono Damelina B. Tambunan Daniel Martomanggolo Wonohadidjojo Darren Wibisono David Boy Tonara David Boy Tonara Eric Harianto Evan Tanuwijaya Goein, Amadeus Michel Hanna Tabita Hasianna Silitonga Hebert Adrianto Heny Arwati I Gusti Ngurah Agung Mahendrayuda Iman Pasu Marganda Hadiarto Purba Indah S Tantular Javier, J Joren Alexander Toding Laura Mahendratta Tjahjono Lestari, Caecilia Citra Liliana Dewi Lindarto, Wira Widjaya Michael Michael Randicha G.S Murpin Josua Sembiring Mychael Maoeretz Engel Nathania Disa Ariesta Andriani Ngurah Tegar Mahardika Patrick Steven Kent Sugiarto Patrick Steven Kent Sugiarto Peter William Richard Delfs Lainama Rinabi Tanamal Rismawati br Sitepu Ronald Torang M. Panggabean Rudi Limantara Satria Adi Nugraha Sharhanata, Alimmada Ammar Soetedja, Venny Sri Subekti Stefanus Rudy Kurniawan Susianto Tri Resmana Susijanto Tri Rasmana Susyanto Tri Rasmana Sutanto, J. E. Tanzilia, May Fanny Theresia Ratih Dewi Saputri Tony Antonio Wahyudi, Alexander Wijanto, Antonnio Benedict Bryan Yehuda, Paul Julian Yobel Nathaniel Filipus