MDDFNet: Mamba-based Dynamic Dual Fusion Network for Traffic Sign Detection

TL;DR

MDDFNet introduces a novel traffic sign detection network using a Mamba-based backbone and dynamic dual fusion to improve feature diversity and handle varying object scales effectively, achieving superior real-time detection performance.

Contribution

The paper presents a new Mamba-based backbone and dynamic dual fusion module that enhance feature extraction and multi-scale detection in traffic sign detection.

Findings

Outperforms state-of-the-art detectors on TT100K dataset.

Maintains real-time processing capabilities.

Effectively detects small traffic signs.

Abstract

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.