Fusing patch-inspired region of interest and gated squeeze-and-excitation attention in ResNet for road classification on mobile devices

Abstract Mobile precision equipment requires efficient and accurate road surface perception for reliable operation. However, existing road classification methods, designed mainly for single-type surfaces, perform poorly on mixed surfaces, limiting system adaptability in complex environments. To address this gap, we constructed a dedicated dataset comprising images of four single-type and two mixed road surfaces, acquired using a custom-built intelligent vehicle equipped with multiple sensors. Each image was meticulously annotated to facilitate model training and evaluation. Leveraging this dataset, we propose a deep learning model, termed GS-ResNet, which integrates squeeze-and-excitation (SE) modules, a gating mechanism, and a region of interest (ROI) extraction inspired by the patch paradigm of Vision Transformers. The SE modules enhance feature discriminability through dynamic channel-wise recalibration, while the gating mechanism refines feature extraction to improve texture perception. The ROI strategy focuses computational resources on the most informative image regions. Our GS-ResNet achieves a classification accuracy of 97.23% with an average response time of 0.5 ms, outperforming state-of-the-art methods in both accuracy and efficiency. This study addresses a critical gap in mixed road surface classification and provides an effective solution for robust road recognition in complex scenarios, offering support for the stable operation of mobile precision equipment across diverse road conditions.