Open Access

ARTICLE

Road Surface Classification Using IMU Data Based on the CGB-Net Deep Learning Architecture

Duong Do The^1,2, Duc-Nghia Tran³, Hoang-Dieu Vu⁴, Manh-Tuyen Vi^4,*, Duc-Tan Tran^4,*

1 Academy of Policy and Development, Hanoi City, Vietnam
2 Graduate University of Sciences and Technology, Vietnam Academy of Science and Technology, Hanoi City, Vietnam
3 Institute of Information Technology, Vietnam Academy of Science and Technology, Hanoi City, Vietnam
4 Faculty of Electrical and Electronic Engineering, Phenikaa University, Hanoi City, Vietnam

* Corresponding Authors: Manh-Tuyen Vi. Email: ; Duc-Tan Tran. Email:

Computers, Materials & Continua 2026, 88(1), 97 https://doi.org/10.32604/cmc.2026.079056

Received 13 January 2026; Accepted 13 April 2026; Issue published 08 May 2026

Abstract

Road-surface identification is important for transportation monitoring and maintenance. However, this task is challenging due to the complexity of vibration signals, feature overlap among different surface types, and variations in real-world operating conditions. These challenges become more significant in time-series classification, where models must achieve high accuracy while remaining computationally efficient and suitable for low-cost hardware. This study investigates the design and evaluation of an automatic road-surface classification system using motion data collected from inertial sensors mounted on a vehicle, including accelerometers and gyroscopes. The system segments synchronized IMU signals into fixed-length windows and assigns each window to a predefined road-surface category. To address this problem, this study proposes CGB-Net, a lightweight and efficient deep learning architecture for road-surface classification. In this architecture, the integration of modules is designed to capture hierarchical features: one-dimensional convolutional neural networks (1D-CNN) are used to extract local temporal features from accelerometer and gyroscope signals, Gated Recurrent Units (GRU) model short-term temporal dependencies, and Bidirectional Long Short-Term Memory (Bi-LSTM) networks capture global long-term temporal context in both directions. The model is trained to distinguish among three types of road surfaces representing different material properties and degradation states: Asphalt_10 (new asphalt, less than 10 years old), Asphalt_15 (aged asphalt, more than 15 years old), and Concrete. Experimental results show that the proposed system achieves high classification performance, with both accuracy and F1-score exceeding 95%. These results indicate strong potential for practical applications in automated road monitoring and maintenance systems.

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Keywords

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