Research Paper#Computer Vision, Deep Learning, Fuzzy Logic, Road Surface Classification🔬 ResearchAnalyzed: Jan 3, 2026 18:50
Road Surface Classification using Deep Learning and Fuzzy Logic
Published:Dec 29, 2025 12:54
•1 min read
•ArXiv
Analysis
This paper addresses the important problem of real-time road surface classification, crucial for autonomous vehicles and traffic management. The use of readily available data like mobile phone camera images and acceleration data makes the approach practical. The combination of deep learning for image analysis and fuzzy logic for incorporating environmental conditions (weather, time of day) is a promising approach. The high accuracy achieved (over 95%) is a significant result. The comparison of different deep learning architectures provides valuable insights.
Key Takeaways
- •Proposes a real-time road surface classification system.
- •Utilizes mobile phone camera images and acceleration data.
- •Employs deep learning (Alexnet, LeNet, VGG, Resnet) for image-based classification.
- •Integrates fuzzy logic to incorporate weather and time-of-day conditions.
- •Achieves high accuracy (over 95%) in classifying road conditions.
Reference
“Achieved over 95% accuracy for road condition classification using deep learning.”