Ma, Yubin (Zhejiang University), Xu, Hang (Zhejiang University), Hu, Simon (Zhejiang University), Hu, Huan (Zhejiang University)

PCAG-Inception: An Enhanced Inceptiontime Model for Pothole Detection in Non-Motorized Vehicle Lanes

Scheduled for presentation during the Regular Session "S06c-Safety, Sensing, and Infrastructure Design for Vulnerable Road Users" (WE-LA-T6), Wednesday, November 19, 2025, 16:20−16:40, Surfers Paradise 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 19, 2025

Abstract

Maintaining high-quality road infrastructure is essential for ensuring traffic safety and minimizing accidents. Consequently, developing a low-cost and efficient road pothole detection and repair system is important to improve road quality and travel safety. In recent studies, vibration signals from cell phone sensors are used for detection. The existing methods usually suffer from high computational complexity and lack of contextual information extraction. To solve these problems, this study proposes a network architecture based on the Inceptiontime framework, PCAG-Inception. Firstly, instead of the conventional operation, we use the ICAM (Inception-based Cross-Attention Module) to operate on only some channels to improve the inference speed. Then, we introduce a cross-attention mechanism with shared weights to establish the relationship between features extracted by convolution at different scales, thus learning a more fine-grained representation. In addition, to better aggregate temporal contextual information, we propose a new gated temporal attention unit. This module captures both local and global features in a time series through a gating mechanism. The gate dynamically adjusts feature weights, enhancing the model's ability to capture interactions between different variables. In this study, a series of experiments are conducted on our constructed vibration signal dataset of road potholes, and the results show that our proposed PCAG-Inception model achieves excellent performance with an accuracy of 94.51%.