SSM-Det: State Space Model-Based Object Detector for Intelligent Transportation System

The State Space Model (SSM) has been a growth of interest in computer vision due to its long-term dependency modeling with linear complexity. Despite massive endeavor, it has not been extensively explored in intelligent transportation system (ITS) yet. In this paper, we propose State Space Model-based object Detector (SSM-Det), that is meticulously curated with Direction-aware Visual State Space Encoder (D-VSSE). Specifically, it customizes multi-path pixel exchange and patch re-arrangement via four-direction scanning mechanism, promoting for information communication. To bridge the information bottleneck across high-low level, we further design Split-Fusion (SF) and Skip-Connection (SC) modules for contextual feature propagation before decoding: SF performs multi-channel semantic separation and re-weighting in global-local scope, while SC is responsible for cross-layer feature interaction in a cascaded manner. Empirical studies is conducted on both VisDrone2019-DET and SEU _PML benchmarks, and our proposed SSM-Det reports the state-of-the-art performance against all counterparts by a substantial margin, while maintaining the real-time inference speed. We hope this work contributes to the in-depth investigation of SSM-based detector for intelligent transportation applications.