[1]张泽宇,郭 斌,张太红*.基于 DCNN 的马匹图像分割算法研究[J].计算机技术与发展,2020,30(10):210-215.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 037]
　ZHANG Ze-yu,GUO Bin,ZHANG Tai-hong.Research on Horse Image Segmentation Algorithm Based on DCNN[J].,2020,30(10):210-215.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 037]
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基于 DCNN 的马匹图像分割算法研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年10期
页码:
210-215
栏目:
应用开发研究
出版日期:
2020-10-10

文章信息/Info

Title:
Research on Horse Image Segmentation Algorithm Based on DCNN
文章编号:
1673-629X(2020)10-0210-06
作者:
张泽宇; 郭 斌; 张太红*
新疆农业大学 计算机与信息工程学院,新疆 乌鲁木齐 830052
Author(s):
ZHANG Ze-yu; GUO Bin; ZHANG Tai-hong
School of Computer and Information Engineering,Xinjiang Agricultural University,Urumqi 830052,China
关键词:
马图像分割; 语义分割; 条件随机场; 深度分离卷积; 迁移学习
Keywords:
horse image segmentation; semantic segmentation; conditional random field; depthwise separable convolution; transfer learn鄄 ing
分类号:
TP183
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 10. 037
文献标志码:
A
摘要:
随着深度学习的发展,卷积神经网络被用于解决各类计算机视觉问题。 物体的分割是图像处理的基础,传统的分 割算法在特定强光噪声场景下对图片的理解能力及工作效率欠佳。 针对传统马匹图像分割算法中存在的分割边缘不清 晰、强光照射下马匹轮廓分割模糊,以及分割前需要进行手工标注等问题,提出一种基于全卷积神经网络并加入条件随机 场的马匹图像分割算法。 建立马匹图像数据集进行训练测试,网络中加入带有空洞卷积的空间金字塔池化模型(ASPP)、 空洞卷积与深度分离卷积使得网络在参数不变的情况下扩大感受野从而有效地分割出马匹。 在深度卷积神经网络 (DCNN)模型上进行迁移学习,并加入全连接条件随机场(CRF)优化分割出来的马匹边缘轮廓。 实验结果表明,该方法在 分割精度上优于传统的马匹图像分割算法,平均交并比(MIOU)达到了 92. 8% 。
Abstract:
With the development of deep learning,convolutional neural network is used to solve many computer vision problems. Image segmentation is the basis of all image processing. Traditional segmentation algorithm has poor understanding of images and work inefficiency under certain high-light noise scene. In view of the problems existing in the traditional horse image segmentation algorithm, such as unclear segmentation edge, fuzzy segmentation of horse contour under strong illumination, and manual annotation before segmentation,we propose a horse image segmentation algorithm based on full convolutional neural network and conditional random field. By establishing horse image dataset to train and adding the atrous spatial pyramid pooling ( ASPP), the atrous convolution and the depthwise separable convolution,it makes the full convolutional neural network model enable the network to expand the receptive field with the parameters unchanged and segment the horse image effectively. The horses are migrated on the deep convolutional neural network (DCNN),and the horse edge contours segmented are added by the fully connected condition random field. The experiment shows that the proposed method is superior to the traditional horse image segmentation algorithm in terms of segmentation accuracy. The mean intersection over union (MIOU) reaches 92. 8% .

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更新日期/Last Update: 2020-10-10