Tang Jiaxi, Zhao Yaqin, Feng Liqi, Zhao Wenxuan
College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Animals (Basel). 2022 Aug 4;12(15):1980. doi: 10.3390/ani12151980.
Camera traps are widely used in wildlife research, conservation, and management, and abundant images are acquired every day. Efficient real-time instance segmentation networks can help ecologists label and study wild animals. However, existing deep convolutional neural networks require a large number of annotations and labels, which makes them unsuitable for small datasets. In this paper, we propose a two-stage method for the instance segmentation of wildlife, including object detection and contour approximation. In the object detection stage, we use FSOD (few-shot object detection) to recognize animal species and detect the initial bounding boxes of animals. In the case of a small wildlife dataset, this method may improve the generalization ability of the wild animal species recognition and even identify new species that only have a small number of training samples. In the second stage, deep snake is used as the contour approximation model for the instance segmentation of wild mammals. The initial bounding boxes generated in the first stage are input to deep snake to approximate the contours of the animal bodies. The model fuses the advantages of detecting new species and real-time instance segmentation. The experimental results show that the proposed method is more suitable for wild animal instance segmentation, in comparison with pixel-wise segmentation methods. In particular, the proposed method shows a better performance when facing challenging images.
相机陷阱在野生动物研究、保护和管理中被广泛使用，每天都会获取大量图像。高效的实时实例分割网络可以帮助生态学家标记和研究野生动物。然而，现有的深度卷积神经网络需要大量的注释和标签，这使得它们不适用于小数据集。在本文中，我们提出了一种用于野生动物实例分割的两阶段方法，包括目标检测和轮廓近似。在目标检测阶段，我们使用少样本目标检测（FSOD）来识别动物物种并检测动物的初始边界框。在野生动物数据集较小的情况下，这种方法可以提高野生动物物种识别的泛化能力，甚至识别出只有少量训练样本的新物种。在第二阶段，深度蛇形模型被用作野生哺乳动物实例分割的轮廓近似模型。第一阶段生成的初始边界框被输入到深度蛇形模型中，以近似动物身体的轮廓。该模型融合了检测新物种和实时实例分割的优点。实验结果表明，与逐像素分割方法相比，所提出的方法更适用于野生动物实例分割。特别是，当面对具有挑战性的图像时，所提出的方法表现出更好的性能。
