MDDFNet: Mamba-based Dynamic Dual Fusion Network for Traffic Sign Detection
This work addresses critical challenges in traffic sign detection for autonomous driving, but it is incremental as it builds on existing object detection methods with specific architectural improvements.
The paper tackled the problem of small object detection, specifically traffic signs, by proposing MDDFNet, which integrates a dynamic dual fusion module and a Mamba-based backbone to enhance feature diversity and handle varying object scales, achieving superior performance on the TT100K dataset while maintaining real-time processing.
The Detection of small objects, especially traffic signs, is a critical sub-task in object detection and autonomous driving. Despite signficant progress in previous research, two main challenges remain. First, the issue of feature extraction being too singular. Second, the detection process struggles to efectively handle objects of varying sizes or scales. These problems are also prevalent in general object detection tasks. To address these challenges, we propose a novel object detection network, Mamba-based Dynamic Dual Fusion Network (MDDFNet), for traffic sign detection. The network integrates a dynamic dual fusion module and a Mamba-based backbone to simultaneously tackle the aforementioned issues. Specifically, the dynamic dual fusion module utilizes multiple branches to consolidate various spatial and semantic information, thus enhancing feature diversity. The Mamba-based backbone leverages global feature fusion and local feature interaction, combining features in an adaptive manner to generate unique classification characteristics. Extensive experiments conducted on the TT100K (Tsinghua-Tencent 100K) datasets demonstrate that MDDFNet outperforms other state-of-the-art detectors, maintaining real-time processing capabilities of single-stage models while achieving superior performance. This confirms the efectiveness of MDDFNet in detecting small traffic signs.