提出植物伪装检测新任务，构建首个相关数据集并设计专用模型。
PlantCamo: Plant Camouflage Detection
- 构建1250张图像的PlantCamo数据集，涵盖58类植物在自然场景中的伪装形态。
- 提出PCNet模型，通过多尺度全局特征增强，在该数据集上显著超越现有方法。
- 填补细粒度伪装检测空白，适合生态智能、计算机视觉领域研究者参考。
伪装目标检测（COD）旨在识别具有伪装特性的物体。尽管已有研究聚焦于自然（动物与昆虫）和非自然（艺术与合成）伪装检测，植物伪装却未受重视。然而，植物伪装在自然界中至关重要。为此，本文提出植物伪装检测（PCD）这一新挑战性问题。为解决该问题，我们构建了PlantCamo数据集，包含1250张图像，涵盖58个物体类别，在多种自然场景中呈现植物伪装。为评估当前技术水平，我们在该数据集上对20余种前沿COD模型进行了大规模基准测试。由于植物伪装具有孔洞和不规则边界等独特特性，我们设计了专用于PCD的PCNet框架。得益于其多尺度全局特征增强与优化机制，该模型性能显著提升。最后，我们讨论了潜在应用与研究启示，期望本工作填补细粒度伪装检测研究空白，推动智能生态学发展。所有资源已公开于https://github.com/yjybuaa/PlantCamo。
原文摘要 · Abstract (English)
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 1,250 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 developed a new framework, named 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.
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