MODA: The First Challenging Benchmark for Multispectral Object Detection in Aerial Images

• URL: http://arxiv.org/abs/2512.09489v1
• Date: Wed, 10 Dec 2025 10:07:06 GMT
• Title: MODA: The First Challenging Benchmark for Multispectral Object Detection in Aerial Images
• Authors: Shuaihao Han, Tingfa Xu, Peifu Liu, Jianan Li,
• Abstract summary: We introduce the first large-scale dataset for Multispectral Object Detection in Aerial images (MODA)<n>This dataset comprises 14,041 MSIs and 330,191 annotations across diverse, challenging scenarios.<n>We also propose OSSDet, a framework that integrates spectral and spatial information with object-aware cues.
• Score: 26.48439423478357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aerial object detection faces significant challenges in real-world scenarios, such as small objects and extensive background interference, which limit the performance of RGB-based detectors with insufficient discriminative information. Multispectral images (MSIs) capture additional spectral cues across multiple bands, offering a promising alternative. However, the lack of training data has been the primary bottleneck to exploiting the potential of MSIs. To address this gap, we introduce the first large-scale dataset for Multispectral Object Detection in Aerial images (MODA), which comprises 14,041 MSIs and 330,191 annotations across diverse, challenging scenarios, providing a comprehensive data foundation for this field. Furthermore, to overcome challenges inherent to aerial object detection using MSIs, we propose OSSDet, a framework that integrates spectral and spatial information with object-aware cues. OSSDet employs a cascaded spectral-spatial modulation structure to optimize target perception, aggregates spectrally related features by exploiting spectral similarities to reinforce intra-object correlations, and suppresses irrelevant background via object-aware masking. Moreover, cross-spectral attention further refines object-related representations under explicit object-aware guidance. Extensive experiments demonstrate that OSSDet outperforms existing methods with comparable parameters and efficiency.

Related papers

• MMOT: The First Challenging Benchmark for Drone-based Multispectral Multi-Object Tracking [30.3437683353074]
  MMOT is the first benchmark for drone-based multispectral multi-object tracking.<n>It features 125 video sequences with over 488.8K annotations across eight categories.<n>To better extract spectral features and leverage oriented annotations, we present a multispectral and orientation-aware MOT scheme.
  arXiv  Detail & Related papers  (2025-10-14T14:25:17Z)
• AuxDet: Auxiliary Metadata Matters for Omni-Domain Infrared Small Target Detection [49.81255045696323]
  We present the Auxiliary Metadata Driven Infrared Small Target Detector (AuxDet)<n>AuxDet integrates metadata semantics with visual features, guiding adaptive representation learning for each sample.<n>Experiments on the challenging WideIRSTD-Full benchmark demonstrate that AuxDet consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-05-21T07:02:05Z)
• Hyperspectral Remote Sensing Images Salient Object Detection: The First Benchmark Dataset and Baseline [14.081609886645555]
  We introduce the first HRSI-SOD dataset, termed HRSSD, which includes 704 hyperspectral images and 5327 pixel-level annotated salient objects.<n>The HRSSD dataset poses substantial challenges for salient object detection algorithms due to large scale variation, diverse foreground-background relations, and multi-salient objects.<n>We propose an innovative and efficient baseline model for HRSI-SOD, termed the Deep Spectral Saliency Network (DSSN)
  arXiv  Detail & Related papers  (2025-04-03T09:12:42Z)
• Hyperspectral Adapter for Object Tracking based on Hyperspectral Video [18.77789707539318]
  A new hyperspectral object tracking method, hyperspectral adapter for tracking (HyA-T), is proposed in this work.<n>The proposed methods extract spectral information directly from the hyperspectral images, which prevent the loss of the spectral information.<n>The HyA-T achieves state-of-the-art performance on all the datasets.
  arXiv  Detail & Related papers  (2025-03-28T07:31:48Z)
• Spectrum-oriented Point-supervised Saliency Detector for Hyperspectral Images [13.79887292039637]
  We introduce point supervision into Hyperspectral salient object detection (HSOD)<n>We incorporate Spectral Saliency, derived from conventional HSOD methods, as a pivotal spectral representation within the framework.<n>We propose a novel pipeline, specifically designed for HSIs, to generate pseudo-labels, effectively mitigating the performance decline associated with point supervision strategy.
  arXiv  Detail & Related papers  (2024-12-24T02:52:43Z)
• Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks [49.84182981950623]
  Multispectral object detection, utilizing RGB and TIR (thermal infrared) modalities, is widely recognized as a challenging task.<n>It requires not only the effective extraction of features from both modalities and robust fusion strategies, but also the ability to address issues such as spectral discrepancies.<n>We introduce an efficient and easily deployable multispectral object detection framework that can seamlessly optimize high-performing single-modality models.
  arXiv  Detail & Related papers  (2024-11-27T12:18:39Z)
• SSF-Net: Spatial-Spectral Fusion Network with Spectral Angle Awareness for Hyperspectral Object Tracking [21.664141982246598]
  Hyperspectral video (HSV) offers valuable spatial, spectral, and temporal information simultaneously.<n>Existing methods primarily focus on band regrouping and rely on RGB trackers for feature extraction.<n>In this paper, a spatial-spectral fusion network with spectral angle awareness (SST-Net) is proposed for hyperspectral (HS) object tracking.
  arXiv  Detail & Related papers  (2024-03-09T09:37:13Z)
• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• Improving Vision Anomaly Detection with the Guidance of Language Modality [64.53005837237754]
  This paper tackles the challenges for vision modality from a multimodal point of view. We propose Cross-modal Guidance (CMG) to tackle the redundant information issue and sparse space issue. To learn a more compact latent space for the vision anomaly detector, CMLE learns a correlation structure matrix from the language modality.
  arXiv  Detail & Related papers  (2023-10-04T13:44:56Z)
• Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation [55.9217962930169]
  We present S2ADet, an object detector that harnesses the rich spectral and spatial complementary information inherent in hyperspectral images. S2ADet surpasses existing state-of-the-art methods, achieving robust and reliable results.
  arXiv  Detail & Related papers  (2023-06-14T09:01:50Z)
• RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images [82.1679766706423]
  Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. We propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs. Our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2021-10-27T07:18:32Z)

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.