MambaNeXt-YOLO: A Hybrid State Space Model for Real-time Object Detection

• URL: http://arxiv.org/abs/2506.03654v2
• Date: Thu, 05 Jun 2025 05:07:11 GMT
• Title: MambaNeXt-YOLO: A Hybrid State Space Model for Real-time Object Detection
• Authors: Xiaochun Lei, Siqi Wu, Weilin Wu, Zetao Jiang,
• Abstract summary: YOLO-series models have set strong benchmarks by balancing speed and accuracy.<n>Transformers have high computational complexity because of their self-attention mechanism.<n>We propose MambaNeXt-YOLO, a novel object detection framework that balances accuracy and efficiency.
• Score: 4.757840725810513
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Real-time object detection is a fundamental but challenging task in computer vision, particularly when computational resources are limited. Although YOLO-series models have set strong benchmarks by balancing speed and accuracy, the increasing need for richer global context modeling has led to the use of Transformer-based architectures. Nevertheless, Transformers have high computational complexity because of their self-attention mechanism, which limits their practicality for real-time and edge deployments. To overcome these challenges, recent developments in linear state space models, such as Mamba, provide a promising alternative by enabling efficient sequence modeling with linear complexity. Building on this insight, we propose MambaNeXt-YOLO, a novel object detection framework that balances accuracy and efficiency through three key contributions: (1) MambaNeXt Block: a hybrid design that integrates CNNs with Mamba to effectively capture both local features and long-range dependencies; (2) Multi-branch Asymmetric Fusion Pyramid Network (MAFPN): an enhanced feature pyramid architecture that improves multi-scale object detection across various object sizes; and (3) Edge-focused Efficiency: our method achieved 66.6% mAP at 31.9 FPS on the PASCAL VOC dataset without any pre-training and supports deployment on edge devices such as the NVIDIA Jetson Xavier NX and Orin NX.

Related papers

• MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone Architecture [12.168520751389622]
  Hyperspectral image (HSI) classification faces challenges such as high-dimensional data, limited training samples, and spectral redundancy.<n>This paper proposes a novel MVNet network architecture that integrates 3D-CNN's local feature extraction, Transformer's global modeling, and Mamba's linear sequence modeling capabilities.<n>On IN, UP, and KSC datasets, MVNet outperforms mainstream hyperspectral image classification methods in both classification accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2025-07-06T14:52:26Z)
• MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection [45.792346999032496]
  We present the first work demonstrating that a pure Mamba block can achieve efficient Dense Global Fusion.<n>Our motivation stems from the observation that existing fusion strategies are constrained by their inability to simultaneously achieve efficiency.<n>We propose height-fidelity LiDAR encoding that preserves precise height information through voxel compression in continuous space.
  arXiv  Detail & Related papers  (2025-07-06T12:29:45Z)
• An Efficient and Mixed Heterogeneous Model for Image Restoration [71.85124734060665]
  Current mainstream approaches are based on three architectural paradigms: CNNs, Transformers, and Mambas.<n>We propose RestorMixer, an efficient and general-purpose IR model based on mixed-architecture fusion.
  arXiv  Detail & Related papers  (2025-04-15T08:19:12Z)
• MobileMamba: Lightweight Multi-Receptive Visual Mamba Network [51.33486891724516]
  Previous research on lightweight models has primarily focused on CNNs and Transformer-based designs. We propose the MobileMamba framework, which balances efficiency and performance. MobileMamba achieves up to 83.6% on Top-1, surpassing existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-24T18:01:05Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Unleashing the Potential of Mamba: Boosting a LiDAR 3D Sparse Detector by Using Cross-Model Knowledge Distillation [22.653014803666668]
  We propose a Faster LiDAR 3D object detection framework, called FASD, which implements heterogeneous model distillation by adaptively uniform cross-model voxel features. We aim to distill the transformer's capacity for high-performance sequence modeling into Mamba models with low FLOPs, achieving a significant improvement in accuracy through knowledge transfer. We evaluated the framework on datasets and nuScenes, achieving a 4x reduction in resource consumption and a 1-2% performance improvement over the current SoTA methods.
  arXiv  Detail & Related papers  (2024-09-17T09:30:43Z)
• MambaVT: Spatio-Temporal Contextual Modeling for robust RGB-T Tracking [51.28485682954006]
  We propose a pure Mamba-based framework (MambaVT) to fully exploit intrinsic-temporal contextual modeling for robust visible-thermal tracking. Specifically, we devise the long-range cross-frame integration component to globally adapt to target appearance variations. Experiments show the significant potential of vision Mamba for RGB-T tracking, with MambaVT achieving state-of-the-art performance on four mainstream benchmarks.
  arXiv  Detail & Related papers  (2024-08-15T02:29:00Z)
• Mamba YOLO: A Simple Baseline for Object Detection with State Space Model [10.44725284994877]
  YOLO series has set a new benchmark for real-time object detectors.<n>Transformer-based structures have emerged as the most powerful solution.<n>However, the quadratic complexity of the self-attentive mechanism increases the computational burden.<n>We introduce a simple yet effective baseline approach called Mamba YOLO.
  arXiv  Detail & Related papers  (2024-06-09T15:56:19Z)
• Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model [18.30032389736101]
  Mamba model, based on state space models (SSM), outperforms Transformer in multiple areas with only linear complexity. We present Mamba3D, a state space model tailored for point cloud learning to enhance local feature extraction.
  arXiv  Detail & Related papers  (2024-04-23T12:20:27Z)
• PointMamba: A Simple State Space Model for Point Cloud Analysis [65.59944745840866]
  We propose PointMamba, transferring the success of Mamba, a recent representative state space model (SSM), from NLP to point cloud analysis tasks. Unlike traditional Transformers, PointMamba employs a linear complexity algorithm, presenting global modeling capacity while significantly reducing computational costs.
  arXiv  Detail & Related papers  (2024-02-16T14:56:13Z)
• Joint Spatial-Temporal and Appearance Modeling with Transformer for Multiple Object Tracking [59.79252390626194]
  We propose a novel solution named TransSTAM, which leverages Transformer to model both the appearance features of each object and the spatial-temporal relationships among objects. The proposed method is evaluated on multiple public benchmarks including MOT16, MOT17, and MOT20, and it achieves a clear performance improvement in both IDF1 and HOTA.
  arXiv  Detail & Related papers  (2022-05-31T01:19:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.