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

TL;DR

This paper introduces MODA, the first large-scale multispectral aerial image dataset, and proposes OSSDet, a novel framework that leverages spectral and spatial cues for improved object detection in challenging aerial scenarios.

Contribution

The paper provides the first comprehensive multispectral aerial object detection dataset and a new detection framework that effectively integrates spectral and spatial information.

Findings

OSSDet outperforms existing methods in accuracy.

MODA dataset enables robust multispectral detection research.

Spectral-spatial integration improves detection in complex scenes.

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.