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PlantCamo: Plant Camouflage Detection

Jinyu Yang1, Qingwei Wang2, Feng Zheng3(), Peng Chen2, Aleš Leonardis4, Deng-Ping Fan5,6
Tapall.ai, Shenzhen 518055, China
College of Computer and Information Technology, China Three Gorges University, Yichang 410073, China
Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK
Nankai International Advanced Research Institute (Shenzhen Futian), Nankai University, Shenzhen 518045, China
Tianjin Key Laboratory of Visual Computing and Intelligence Perception (VCIP), Nankai University, Tianjin 300350, China

Jinyu Yang and Qingwei Wang contributed equally to this work.

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Abstract

Camouflaged Object Detection (COD) aims to detect objects with camouflaged properties. Although previous studies have focused on natural (animals and insects) and unnatural (artistic and synthetic) camouflage detection, plant camouflage has been neglected. However, plant camouflage plays a vital role in natural camouflage. Therefore, this paper introduces a new challenging problem of Plant Camouflage Detection (PCD). To address this problem, we introduce the PlantCamo dataset, which comprises 1250 images with camouflaged plants representing 58 object categories in various natural scenes. To investigate the current status of plant camouflage detection, we conduct a large-scale benchmark study using 20+ cutting-edge COD models on the proposed dataset. Due to the unique characteristics of plant camouflage, including holes and irregular borders, we develope a new framework, PCNet, dedicated to PCD. Our PCNet surpasses performance thanks to its multi-scale global feature enhancement and refinement. Finally, we discuss the potential applications and insights, hoping this work fills the gap in fine-grained COD research and facilitates further intelligent ecology research. All resources will be available on https://github.com/yjybuaa/PlantCamo.

Keywords

plant camouflage detectioncamouflaged object detectioncamouflaged object segmentation

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CAAI Artificial Intelligence Research
Volume 4,
2025
Article number: 9150045
DOI: 10.26599/AIR.2025.9150045
Cite this article:
Yang J, Wang Q, Zheng F, et al. PlantCamo: Plant Camouflage Detection. CAAI Artificial Intelligence Research, 2025, 4: 9150045. https://doi.org/10.26599/AIR.2025.9150045
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