CN107240087A - 目标检测系统和方法 - Google Patents
目标检测系统和方法 Download PDFInfo
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- CN107240087A CN107240087A CN201611033218.2A CN201611033218A CN107240087A CN 107240087 A CN107240087 A CN 107240087A CN 201611033218 A CN201611033218 A CN 201611033218A CN 107240087 A CN107240087 A CN 107240087A
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- 238000001514 detection method Methods 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000003066 decision tree Methods 0.000 claims abstract description 83
- 238000004364 calculation method Methods 0.000 claims abstract description 18
- 238000011156 evaluation Methods 0.000 claims abstract description 14
- 238000013528 artificial neural network Methods 0.000 claims abstract description 13
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- 230000001186 cumulative effect Effects 0.000 claims description 4
- 238000004220 aggregation Methods 0.000 claims 2
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- 238000013527 convolutional neural network Methods 0.000 description 43
- 239000010410 layer Substances 0.000 description 29
- 238000007637 random forest analysis Methods 0.000 description 17
- 230000007935 neutral effect Effects 0.000 description 10
- 210000002569 neuron Anatomy 0.000 description 8
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- 238000012360 testing method Methods 0.000 description 7
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24133—Distances to prototypes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
- G06N3/061—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using biological neurons, e.g. biological neurons connected to an integrated circuit
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
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CN201611033218.2A CN107240087B (zh) | 2016-11-01 | 2016-11-01 | 目标检测系统和方法 |
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CN201611033218.2A CN107240087B (zh) | 2016-11-01 | 2016-11-01 | 目标检测系统和方法 |
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CN107240087B CN107240087B (zh) | 2020-04-24 |
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Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107944369A (zh) * | 2017-11-17 | 2018-04-20 | 大连大学 | 一种基于级联区域生成网络和增强随机森林的行人检测方法 |
CN108022257A (zh) * | 2017-12-28 | 2018-05-11 | 中国科学院半导体研究所 | 适用于硬件的高速卷积神经网络目标跟踪方法和装置 |
CN108460336A (zh) * | 2018-01-29 | 2018-08-28 | 南京邮电大学 | 一种基于深度学习的行人检测方法 |
CN110310114A (zh) * | 2018-03-27 | 2019-10-08 | 阿里巴巴集团控股有限公司 | 对象分类方法、装置、服务器及存储介质 |
CN110555354A (zh) * | 2018-05-31 | 2019-12-10 | 北京深鉴智能科技有限公司 | 特征筛选方法和装置、目标检测方法和设备、电子设备及存储介质 |
CN111008544A (zh) * | 2018-10-08 | 2020-04-14 | 阿里巴巴集团控股有限公司 | 交通监控和无人驾驶辅助系统以及目标检测方法及设备 |
CN111144373A (zh) * | 2019-12-31 | 2020-05-12 | 广州市昊链信息科技股份有限公司 | 一种信息识别方法、装置、计算机设备和存储介质 |
CN111417067A (zh) * | 2020-03-13 | 2020-07-14 | 智慧足迹数据科技有限公司 | 定位用户到访位置的方法和装置 |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103366180A (zh) * | 2013-06-14 | 2013-10-23 | 山东大学 | 一种基于自动特征学习的细胞图像分割方法 |
CN103902591A (zh) * | 2012-12-27 | 2014-07-02 | 中国科学院深圳先进技术研究院 | 构建决策树分类器的方法及装置 |
CN104778670A (zh) * | 2015-04-17 | 2015-07-15 | 广西科技大学 | 一种基于多元统计模型的分形小波自适应图像去噪方法 |
CN105551028A (zh) * | 2015-12-09 | 2016-05-04 | 中山大学 | 一种基于遥感影像的地理空间数据动态更新的方法及系统 |
-
2016
- 2016-11-01 CN CN201611033218.2A patent/CN107240087B/zh active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103902591A (zh) * | 2012-12-27 | 2014-07-02 | 中国科学院深圳先进技术研究院 | 构建决策树分类器的方法及装置 |
CN103366180A (zh) * | 2013-06-14 | 2013-10-23 | 山东大学 | 一种基于自动特征学习的细胞图像分割方法 |
CN104778670A (zh) * | 2015-04-17 | 2015-07-15 | 广西科技大学 | 一种基于多元统计模型的分形小波自适应图像去噪方法 |
CN105551028A (zh) * | 2015-12-09 | 2016-05-04 | 中山大学 | 一种基于遥感影像的地理空间数据动态更新的方法及系统 |
Cited By (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107944369A (zh) * | 2017-11-17 | 2018-04-20 | 大连大学 | 一种基于级联区域生成网络和增强随机森林的行人检测方法 |
CN108022257A (zh) * | 2017-12-28 | 2018-05-11 | 中国科学院半导体研究所 | 适用于硬件的高速卷积神经网络目标跟踪方法和装置 |
CN108460336A (zh) * | 2018-01-29 | 2018-08-28 | 南京邮电大学 | 一种基于深度学习的行人检测方法 |
CN110310114A (zh) * | 2018-03-27 | 2019-10-08 | 阿里巴巴集团控股有限公司 | 对象分类方法、装置、服务器及存储介质 |
US10692089B2 (en) | 2018-03-27 | 2020-06-23 | Alibaba Group Holding Limited | User classification using a deep forest network |
CN110555354A (zh) * | 2018-05-31 | 2019-12-10 | 北京深鉴智能科技有限公司 | 特征筛选方法和装置、目标检测方法和设备、电子设备及存储介质 |
CN110555354B (zh) * | 2018-05-31 | 2022-06-17 | 赛灵思电子科技(北京)有限公司 | 特征筛选方法和装置、目标检测方法和设备、电子设备及存储介质 |
CN111008544A (zh) * | 2018-10-08 | 2020-04-14 | 阿里巴巴集团控股有限公司 | 交通监控和无人驾驶辅助系统以及目标检测方法及设备 |
CN111008544B (zh) * | 2018-10-08 | 2023-05-09 | 阿里巴巴集团控股有限公司 | 交通监控和无人驾驶辅助系统以及目标检测方法及设备 |
CN111144373A (zh) * | 2019-12-31 | 2020-05-12 | 广州市昊链信息科技股份有限公司 | 一种信息识别方法、装置、计算机设备和存储介质 |
CN111144373B (zh) * | 2019-12-31 | 2020-12-04 | 广州市昊链信息科技股份有限公司 | 一种信息识别方法、装置、计算机设备和存储介质 |
CN111417067A (zh) * | 2020-03-13 | 2020-07-14 | 智慧足迹数据科技有限公司 | 定位用户到访位置的方法和装置 |
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