HyperCOD: The First Challenging Benchmark and Baseline for Hyperspectral Camouflaged Object Detection
Shuyan Bai, Tingfa Xu, Peifu Liu, Yuhao Qiu, Huiyan Bai, Huan Chen, Yanyan Peng, Jianan Li
Abstract
RGB-based camouflaged object detection struggles in real-world scenarios where color and texture cues are ambiguous. While hyperspectral image offers a powerful alternative by capturing fine-grained spectral signatures, progress in hyperspectral camouflaged object detection (HCOD) has been critically hampered by the absence of a dedicated, large-scale benchmark. To spur innovation, we introduce HyperCOD, the first challenging benchmark for HCOD. Comprising 350 high-resolution hyperspectral images, It features complex real-world scenarios with minimal objects, intricate shapes, severe occlusions, and dynamic lighting to challenge current models.The advent of foundation models like the Segment Anything Model (SAM) presents a compelling opportunity. To adapt the Segment Anything Model (SAM) for HCOD, we propose HyperSpectral Camouflage-aware SAM (HSC-SAM). HSC-SAM ingeniously reformulates the hyperspectral image by decoupling it into a spatial map fed to SAM
BibTeX
@inproceedings{aaai2026_hypercodthefirst,
title = {HyperCOD: The First Challenging Benchmark and Baseline for Hyperspectral Camouflaged Object Detection},
author = {Shuyan Bai and Tingfa Xu and Peifu Liu and Yuhao Qiu and Huiyan Bai and Huan Chen and Yanyan Peng and Jianan Li},
booktitle = {AAAI 2026},
year = {2026}
}