[1]邓杰航,何冬冬,卓家鸿,等.复杂背景干扰下硅藻图像的深度网络识别与定位[J].南方医科大学学报,2020,(02):183-189.[doi:10.12122/j.issn.1673-4254.2020.02.03]
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复杂背景干扰下硅藻图像的深度网络识别与定位()
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《南方医科大学学报》[ISSN:1673-4254/CN:44-1627/R]

卷:
期数:
2020年02期
页码:
183-189
栏目:
出版日期:
2020-02-29

文章信息/Info

Title:
Deep learning network-based recognition and localization of diatom images against complex background
作者:
邓杰航何冬冬卓家鸿赵 建肖 成康晓东胡孙林顾国生刘 超
关键词:
深度学习复杂背景硅藻目标检测机器学习
Keywords:
deep learning complex background diatom object detection machine learning
DOI:
10.12122/j.issn.1673-4254.2020.02.03
文献标志码:
A
摘要:
目的 提出一种刑侦尸检中基于深度学习网络的受复杂背景干扰的硅藻目标自动识别与定位方法。方法 主要由两大模块组成,分别是初步定位与精确定位模块。在初步定位模块中,应用ZFNet的卷积层、池化层提取高层次的硅藻特征,然后应用RPN(Region Proposal Network)生成可能存在硅藻的区域并且初步完成硅藻目标的定位。在精确定位中,应用Fast R-CNN精确修改硅藻位置信息与识别硅藻类别。结果 应用简单、部分复杂与复杂背景的自建库图像对传统机器学习方法与本文方法进行实验验证,传统识别方法对有部分背景干扰的硅藻图片识别率约为60%,且不能识别与定位受复杂背景干扰的硅藻图像。本文方法能够有效识别与定位复杂背景下硅藻图像中的多种目标,且平均识别率达到85%。结论 本文方法能够应用于刑侦尸检中识别与定位复杂背景干扰的硅藻图像中的目标。
Abstract:
Objective We propose a deep learning network-based method for recognizing and locating diatom targets with interference by complex background in autopsy. Method The system consisted of two modules: the preliminary positioning module and the accurate positioning module. In preliminary positioning, ZFNet convolution and pooling were utilized to extract the high-level features, and Regional Proposal Network (RPN) was applied to generate the regions where the diatoms may exist. In accurate positioning, Fast R-CNN was used to modify the position information and identify the types of the diatoms. Results We compared the proposed method with conventional machine learning methods using a self-built database of images with interference by simple, moderate and complex backgrounds. The conventional methods showed a recognition rate of diatoms against partial background interference of about 60%, and failed to recognize or locate the diatom objects in the datasets with complex background interference. The deep learning network-based method effectively recognized and located the diatom targets against complex background interference with an average recognition rate reaching 85%. Conclusion The proposed method can be applied for recognition and location of diatom targets against complex background interference in autopsy.

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[1]梁翠霞,李明强,边兆英,等.基于深度学习特征的乳腺肿瘤分类模型评估[J].南方医科大学学报,2019,(01):88.[doi:10.12122/j.issn.1673-4254.2019.01.14]
[2]邓力,傅蓉.基于心拍的端到端心律失常分类[J].南方医科大学学报,2019,(09):1071.[doi:10.12122/j.issn.1673-4254.2019.09.11]

更新日期/Last Update: 2020-03-14