E2E-MFD: Towards End-to-End Synchronous Multimodal Fusion Detection

• URL: http://arxiv.org/abs/2403.09323v3
• Date: Thu, 23 May 2024 04:23:49 GMT
• Title: E2E-MFD: Towards End-to-End Synchronous Multimodal Fusion Detection
• Authors: Jiaqing Zhang, Mingxiang Cao, Xue Yang, Weiying Xie, Jie Lei, Daixun Li, Wenbo Huang, Yunsong Li,
• Abstract summary: We introduce E2E-MFD, a novel end-to-end algorithm for multimodal fusion detection. E2E-MFD streamlines the process, achieving high performance with a single training phase. Our extensive testing on multiple public datasets reveals E2E-MFD's superior capabilities.
• Score: 21.185032466325737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal image fusion and object detection are crucial for autonomous driving. While current methods have advanced the fusion of texture details and semantic information, their complex training processes hinder broader applications. Addressing this challenge, we introduce E2E-MFD, a novel end-to-end algorithm for multimodal fusion detection. E2E-MFD streamlines the process, achieving high performance with a single training phase. It employs synchronous joint optimization across components to avoid suboptimal solutions tied to individual tasks. Furthermore, it implements a comprehensive optimization strategy in the gradient matrix for shared parameters, ensuring convergence to an optimal fusion detection configuration. Our extensive testing on multiple public datasets reveals E2E-MFD's superior capabilities, showcasing not only visually appealing image fusion but also impressive detection outcomes, such as a 3.9% and 2.0% mAP50 increase on horizontal object detection dataset M3FD and oriented object detection dataset DroneVehicle, respectively, compared to state-of-the-art approaches. The code is released at https://github.com/icey-zhang/E2E-MFD.

Related papers

• SeaDATE: Remedy Dual-Attention Transformer with Semantic Alignment via Contrast Learning for Multimodal Object Detection [18.090706979440334]
  Multimodal object detection leverages diverse modal information to enhance the accuracy and robustness of detectors. Current methods merely stack Transformer-guided fusion techniques without exploring their capability to extract features at various depth layers of network. In this paper, we introduce an accurate and efficient object detection method named SeaDATE.
  arXiv  Detail & Related papers  (2024-10-15T07:26:39Z)
• Progressive Multi-Modal Fusion for Robust 3D Object Detection [12.048303829428452]
  Existing methods perform sensor fusion in a single view by projecting features from both modalities either in Bird's Eye View (BEV) or Perspective View (PV) We propose ProFusion3D, a progressive fusion framework that combines features in both BEV and PV at both intermediate and object query levels. Our architecture hierarchically fuses local and global features, enhancing the robustness of 3D object detection.
  arXiv  Detail & Related papers  (2024-10-09T22:57:47Z)
• Fusion-Mamba for Cross-modality Object Detection [63.56296480951342]
  Cross-modality fusing information from different modalities effectively improves object detection performance. We design a Fusion-Mamba block (FMB) to map cross-modal features into a hidden state space for interaction. Our proposed approach outperforms the state-of-the-art methods on $m$AP with 5.9% on $M3FD$ and 4.9% on FLIR-Aligned datasets.
  arXiv  Detail & Related papers  (2024-04-14T05:28:46Z)
• PoIFusion: Multi-Modal 3D Object Detection via Fusion at Points of Interest [65.48057241587398]
  PoIFusion is a framework to fuse information of RGB images and LiDAR point clouds at the points of interest (PoIs) Our approach maintains the view of each modality and obtains multi-modal features by computation-friendly projection and computation. We conducted extensive experiments on nuScenes and Argoverse2 datasets to evaluate our approach.
  arXiv  Detail & Related papers  (2024-03-14T09:28:12Z)
• MLF-DET: Multi-Level Fusion for Cross-Modal 3D Object Detection [54.52102265418295]
  We propose a novel and effective Multi-Level Fusion network, named as MLF-DET, for high-performance cross-modal 3D object DETection. For the feature-level fusion, we present the Multi-scale Voxel Image fusion (MVI) module, which densely aligns multi-scale voxel features with image features. For the decision-level fusion, we propose the lightweight Feature-cued Confidence Rectification (FCR) module, which exploits image semantics to rectify the confidence of detection candidates.
  arXiv  Detail & Related papers  (2023-07-18T11:26:02Z)
• 3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D Point Clouds [95.54285993019843]
  We propose a method for joint detection and tracking of multiple objects in 3D point clouds. Our model exploits temporal information employing multiple frames to detect objects and track them in a single network.
  arXiv  Detail & Related papers  (2022-11-01T20:59:38Z)
• MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection [89.26380781863665]
  Fusing LiDAR and camera information is essential for achieving accurate and reliable 3D object detection in autonomous driving systems. Recent approaches aim at exploring the semantic densities of camera features through lifting points in 2D camera images into 3D space for fusion. We propose a novel framework that focuses on the multi-scale progressive interaction of the multi-granularity LiDAR and camera features.
  arXiv  Detail & Related papers  (2022-09-07T12:29:29Z)
• MBDF-Net: Multi-Branch Deep Fusion Network for 3D Object Detection [17.295359521427073]
  We propose a Multi-Branch Deep Fusion Network (MBDF-Net) for 3D object detection. In the first stage, our multi-branch feature extraction network utilizes Adaptive Attention Fusion modules to produce cross-modal fusion features from single-modal semantic features. In the second stage, we use a region of interest (RoI) -pooled fusion module to generate enhanced local features for refinement.
  arXiv  Detail & Related papers  (2021-08-29T15:40:15Z)
• EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic Segmentation [62.210091681352914]
  We study multi-sensor fusion for 3D semantic segmentation for many applications, such as autonomous driving and robotics. In this work, we investigate a collaborative fusion scheme called perception-aware multi-sensor fusion (PMF) We propose a two-stream network to extract features from the two modalities separately. The extracted features are fused by effective residual-based fusion modules.
  arXiv  Detail & Related papers  (2021-06-21T10:47:26Z)

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.