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All Journal Malcom: Indonesian Journal of Machine Learning and Computer Science
Damayanti, Iren
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Computer Science & IT

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Deploying Edge AI for Road Surface Damage Detection Using TensorFlow Lite and Teachable Machine Damayanti, Iren; Putra, Zico Pratama
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 4 (2025): MALCOM October 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i4.2067

Abstract

Road damage compromises transportation safety and drives high infrastructure maintenance costs. To address the limitations of traditional methods, which are expensive and non-scalable, this study proposes an Edge AI alternative utilizing widely available smartphones and machine learning capabilities. We present a real-time road damage detection system powered by TensorFlow Lite and Teachable Machine. The system architecture employs lightweight CNN models (such as MobileNet and EfficientNet Lite) optimized for edge deployment. This implementation enables the immediate detection of anomalies (cracks and potholes) directly on the mobile device without cloud dependency, ensuring low latency. Testing demonstrated robust model performance. For pothole detection, the system achieved an accuracy of 95%; for cracks, the accuracy was 92%. During real-time trials in daytime urban settings, the system achieved an average detection latency of 200 milliseconds with an accuracy of 94%. This user-friendly system also supports data collection via crowdsourcing, facilitating comprehensive infrastructure monitoring and proactive maintenance. This innovation offers a scalable, cost-effective, and user-friendly solution with significant potential to advance transportation safety and maintenance efficiency.
Co-Authors Putra, Zico Pratama