论文标题
边界引导的伪装对象检测
Boundary-Guided Camouflaged Object Detection
论文作者
论文摘要
伪装的对象检测（COD），将其优雅地融合到周围环境中的对象是一项有价值但充满挑战的任务。现有的深度学习方法通常陷入了具有完整和精细的对象结构准确识别伪装对象的困难。为此，在本文中，我们提出了一个新颖的边界引导网络（BGNET），以伪装对象检测。我们的方法探索了有价值的和额外的对象相关的边缘语义，以指导COD的表示形式，这迫使模型生成突出对象结构的特征，从而促进了精确边界定位的伪装对象检测。在三个具有挑战性的基准数据集上进行的广泛实验表明，我们的BGNET在四个广泛使用的评估指标下的现有18种最先进的方法明显优于现有的18种最新方法。我们的代码可公开可用：https：//github.com/thograce/bgnet。
Camouflaged object detection (COD), segmenting objects that are elegantly blended into their surroundings, is a valuable yet challenging task. Existing deep-learning methods often fall into the difficulty of accurately identifying the camouflaged object with complete and fine object structure. To this end, in this paper, we propose a novel boundary-guided network (BGNet) for camouflaged object detection. Our method explores valuable and extra object-related edge semantics to guide representation learning of COD, which forces the model to generate features that highlight object structure, thereby promoting camouflaged object detection of accurate boundary localization. Extensive experiments on three challenging benchmark datasets demonstrate that our BGNet significantly outperforms the existing 18 state-of-the-art methods under four widely-used evaluation metrics. Our code is publicly available at: https://github.com/thograce/BGNet.
