# MCOD: The First Challenging Benchmark for Multispectral Camouflaged Object Detection

arXiv: https://arxiv.org/abs/2509.15753 · HTML: https://mlsanity.com/paper/2509.15753/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [Structure-measure: A New Way to Evaluate Foreground Maps](https://mlsanity.com/paper/1708.00786/) (2017)
- [Enhanced-alignment Measure for Binary Foreground Map Evaluation](https://mlsanity.com/paper/1805.10421/) (2018)
- [Anabranch Network for Camouflaged Object Segmentation](https://mlsanity.com/paper/2105.09451/) (2021)
- [Concealed Object Detection](https://mlsanity.com/paper/2102.10274/) (2021)
- [Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2004.14133/) (2020)
- [Salient Object Detection: A Benchmark](https://mlsanity.com/paper/1501.02741/) (2015)
- [Towards High-Resolution Salient Object Detection](https://mlsanity.com/paper/1908.07274/) (2019)
- [Context-aware Cross-level Fusion Network for Camouflaged Object Detection](https://mlsanity.com/paper/2105.12555/) (2021)
- [VSCode: General Visual Salient and Camouflaged Object Detection with 2D Prompt Learning](https://mlsanity.com/paper/2311.15011/) (2023)
- [Source-free Depth for Object Pop-out](https://mlsanity.com/paper/2212.05370/) (2022)
- [Advances in Deep Concealed Scene Understanding](https://mlsanity.com/paper/2304.11234/) (2023)
- [Relax Image-Specific Prompt Requirement in SAM: A Single Generic Prompt for Segmenting Camouflaged Objects](https://mlsanity.com/paper/2312.07374/) (2023)
- [A Survey of Camouflaged Object Detection and Beyond](https://mlsanity.com/paper/2408.14562/) (2024)
- [Camouflaged Object Detection via Context-aware Cross-level Fusion](https://mlsanity.com/paper/2207.13362/) (2022)

## Built on by (most cited first, 3 of 3)

- [Nested Unfolding Network for Real-World Concealed Object Segmentation](https://mlsanity.com/paper/2511.18164/) (2025)
- [Evaluating an Adaptive Multispectral Turret System for Autonomous Tracking Across Variable Illumination Conditions](https://mlsanity.com/paper/2512.22263/) (2025)
- [Seeing the Unseen: Camouflaged Object Detection Beyond the Visible Spectrum](https://mlsanity.com/paper/2608.30355/) (2026)

## Abstract (arXiv)

Camouflaged Object Detection (COD) aims to identify objects that blend seamlessly into natural scenes. Although RGB-based methods have advanced, their performance remains limited under challenging conditions. Multispectral imagery, providing rich spectral information, offers a promising alternative for enhanced foreground-background discrimination. However, existing COD benchmark datasets are exclusively RGB-based, lacking essential support for multispectral approaches, which has impeded progress in this area. To address this gap, we introduce MCOD, the first challenging benchmark dataset specifically designed for multispectral camouflaged object detection. MCOD features three key advantages: (i) Comprehensive challenge attributes: It captures real-world difficulties such as small object sizes and extreme lighting conditions commonly encountered in COD tasks. (ii) Diverse real-world scenarios: The dataset spans a wide range of natural environments to better reflect practical applications. (iii) High-quality pixel-level annotations: Each image is manually annotated with precise object masks and corresponding challenge attribute labels. We benchmark eleven representative COD methods on MCOD, observing a consistent performance drop due to increased task difficulty. Notably, integrating multispectral modalities substantially alleviates this degradation, highlighting the value of spectral information in enhancing detection robustness. We anticipate MCOD will provide a strong foundation for future research in multispectral camouflaged object detection. The dataset is publicly accessible at https://github.com/yl2900260-bit/MCOD.