Abstract Automatic identification of butterfly species has attracted more and more attention due to the increasing demand for the accuracy and timeliness of butterfly species identification. Since the butterfly images we captured are usually ecological images, which not only have butterflies but also contain many irrelevant objects, such as leaves, flowers and other complex backgrounds. Therefore, segmenting butterflies from their ecological images is an issue that needs to be addressed prior to the tasks of identification and the segmentation quality directly affects the identification effect. However, the huge differences in butterflies, and the complexity of the natural environment make it very challenging to accurately segment butterflies from ecological images. Deep learning based methods are more promising for butterfly ecological image segmentation than traditional methods because they have powerful feature learning and representation ability. However, butterfly segmentation is still challenging when complex background interference occurs in images. To address this issue, we propose a dilated encoder network to capture more high-level features and get high-resolution output, which is both lightweight and accurate for automatic butterfly ecological image segmentation. In addition, we adopt the dice coefficient loss function to better balance the butterfly and non-butterfly regions. Experimental results on the public Leeds Butterfly dataset demonstrate that our method outperforms the state-of-the-art deep learning based image segmentation approaches.

中文翻译：

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生态图像中蝴蝶自动分割的深度学习技术

摘要 由于对蝴蝶种类识别的准确性和及时性的需求日益增加，蝴蝶种类的自动识别越来越受到关注。由于我们拍摄的蝴蝶图像通常是生态图像，其中不仅有蝴蝶，还包含许多不相关的物体，如树叶、花朵等复杂背景。因此，从生态图像中分割蝴蝶是识别任务之前需要解决的问题，分割质量直接影响识别效果。然而，蝴蝶的巨大差异和自然环境的复杂性使得从生态图像中准确地分割出蝴蝶非常具有挑战性。基于深度学习的方法在蝴蝶生态图像分割方面比传统方法更有前景，因为它们具有强大的特征学习和表示能力。然而，当图像中出现复杂的背景干扰时，蝴蝶分割仍然具有挑战性。为了解决这个问题，我们提出了一个扩张的编码器网络来捕获更多高级特征并获得高分辨率输出，这对于自动蝴蝶生态图像分割既轻巧又准确。此外，我们采用骰子系数损失函数来更好地平衡蝴蝶和非蝴蝶区域。在公共 Leeds Butterfly 数据集上的实验结果表明，我们的方法优于最先进的基于深度学习的图像分割方法。