Hierarchical Sparse-Routing-Enabled Mamba Scanning for Tire Pattern Recognition

Abstract

To tackle the challenging representation problem of fine-grained tire tread patterns, we present a hierarchical sparse-routing-enabled multi-mode multi-directional Mamba (HSR-Mamba) scanning framework for tire pattern recognition to improve performance, and it mainly consists of a fine-grained textural MoE based on four-directional pixel scanning and a coarse-grained structural Mixture-of-Experts (MoE) based on three global patch traversals. It simultaneously realizes micro multi-directional fine-grained texture modeling and macro multi-mode global structure modeling. Additionally, hierarchical sparse routing is adopted to eliminate redundant forward computations, which effectively balances recognition accuracy and inference efficiency through correlated activation mechanism of a dual-layer expert system. In addition, a series of experimental results on CIIP-TPID-V1.1 have verified its effectiveness.