Deep learning techniques for automatic butterfly segmentation in ecological images

蝴蝶 分割 人工智能 计算机科学 图像分割 鉴定(生物学) 深度学习 市场细分 自编码 计算机视觉 模式识别(心理学) 机器学习 生态学 生物 营销 业务
作者
Hui Tang,Bin Wang,Xin Chen
出处
期刊:Computers and Electronics in Agriculture [Elsevier BV]
卷期号:178: 105739-105739 被引量:28
标识
DOI:10.1016/j.compag.2020.105739
摘要

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.
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