CN115330740B - 一种基于mdcn的轻量化裂缝识别方法 - Google Patents
一种基于mdcn的轻量化裂缝识别方法 Download PDFInfo
- Publication number
- CN115330740B CN115330740B CN202211007036.3A CN202211007036A CN115330740B CN 115330740 B CN115330740 B CN 115330740B CN 202211007036 A CN202211007036 A CN 202211007036A CN 115330740 B CN115330740 B CN 115330740B
- Authority
- CN
- China
- Prior art keywords
- mdcn
- downsampling
- module
- convolution
- crack
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000000605 extraction Methods 0.000 claims abstract description 23
- 238000001514 detection method Methods 0.000 claims abstract description 21
- 238000013507 mapping Methods 0.000 claims abstract description 7
- 238000010586 diagram Methods 0.000 claims abstract description 5
- 230000004913 activation Effects 0.000 claims description 14
- 238000004364 calculation method Methods 0.000 claims description 10
- 230000004927 fusion Effects 0.000 claims description 10
- 230000007246 mechanism Effects 0.000 claims description 8
- 238000012549 training Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 5
- 238000009825 accumulation Methods 0.000 claims description 2
- 238000013341 scale-up Methods 0.000 claims description 2
- 230000006870 function Effects 0.000 description 19
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000013135 deep learning Methods 0.000 description 2
- 230000010339 dilation Effects 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- ORILYTVJVMAKLC-UHFFFAOYSA-N Adamantane Natural products C1C(C2)CC3CC1CC2C3 ORILYTVJVMAKLC-UHFFFAOYSA-N 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000005336 cracking Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 230000003902 lesion Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000001179 sorption measurement Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 230000001629 suppression Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 239000013585 weight reducing agent Substances 0.000 description 1
Classifications
-
- 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
- G06T7/0004—Industrial image inspection
-
- 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
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/07—Target detection
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Software Systems (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- Medical Informatics (AREA)
- Multimedia (AREA)
- Databases & Information Systems (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Quality & Reliability (AREA)
- Data Mining & Analysis (AREA)
- Molecular Biology (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Image Analysis (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
Description
Claims (8)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202211007036.3A CN115330740B (zh) | 2022-08-22 | 2022-08-22 | 一种基于mdcn的轻量化裂缝识别方法 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202211007036.3A CN115330740B (zh) | 2022-08-22 | 2022-08-22 | 一种基于mdcn的轻量化裂缝识别方法 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN115330740A CN115330740A (zh) | 2022-11-11 |
CN115330740B true CN115330740B (zh) | 2023-08-08 |
Family
ID=83925910
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202211007036.3A Active CN115330740B (zh) | 2022-08-22 | 2022-08-22 | 一种基于mdcn的轻量化裂缝识别方法 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN115330740B (zh) |
Citations (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2011242365A (ja) * | 2010-05-21 | 2011-12-01 | Nippon Telegr & Teleph Corp <Ntt> | コンクリート構造物画像のひび割れ検知装置、ひび割れ検知方法及びそのプログラム |
CN104007175A (zh) * | 2014-05-09 | 2014-08-27 | 华南理工大学 | 一种悬臂柔性梁多裂缝损伤识别装置及方法 |
CN110222386A (zh) * | 2019-05-23 | 2019-09-10 | 河海大学常州校区 | 一种行星齿轮退化状态识别方法 |
CN110544251A (zh) * | 2019-09-08 | 2019-12-06 | 刘凡 | 基于多迁移学习模型融合的大坝裂缝检测方法 |
EP3596449A1 (en) * | 2017-03-14 | 2020-01-22 | University of Manitoba | Structure defect detection using machine learning algorithms |
CN111739052A (zh) * | 2020-06-19 | 2020-10-02 | 山东凯鑫宏业生物科技有限公司 | 应用于医疗的基于自适应轮廓模型肺部mri图像分割方法及mri设备 |
CN112259223A (zh) * | 2020-10-22 | 2021-01-22 | 河北工业大学 | 基于全视野数字切片的病人级别肿瘤智能诊断方法 |
CN113421187A (zh) * | 2021-06-10 | 2021-09-21 | 山东师范大学 | 一种超分辨率重建方法、系统、存储介质、设备 |
CN113674247A (zh) * | 2021-08-23 | 2021-11-19 | 河北工业大学 | 一种基于卷积神经网络的x射线焊缝缺陷检测方法 |
CN113822880A (zh) * | 2021-11-22 | 2021-12-21 | 中南大学 | 一种基于深度学习的裂缝识别方法 |
CN114623776A (zh) * | 2022-05-16 | 2022-06-14 | 四川省公路规划勘察设计研究院有限公司 | 基于隧道变形监测的隧道损伤预测方法 |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20210350517A1 (en) * | 2020-05-08 | 2021-11-11 | The Board Of Trustees Of The University Of Alabama | Robust roadway crack segmentation using encoder-decoder networks with range images |
-
2022
- 2022-08-22 CN CN202211007036.3A patent/CN115330740B/zh active Active
Patent Citations (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2011242365A (ja) * | 2010-05-21 | 2011-12-01 | Nippon Telegr & Teleph Corp <Ntt> | コンクリート構造物画像のひび割れ検知装置、ひび割れ検知方法及びそのプログラム |
CN104007175A (zh) * | 2014-05-09 | 2014-08-27 | 华南理工大学 | 一种悬臂柔性梁多裂缝损伤识别装置及方法 |
EP3596449A1 (en) * | 2017-03-14 | 2020-01-22 | University of Manitoba | Structure defect detection using machine learning algorithms |
CN110222386A (zh) * | 2019-05-23 | 2019-09-10 | 河海大学常州校区 | 一种行星齿轮退化状态识别方法 |
CN110544251A (zh) * | 2019-09-08 | 2019-12-06 | 刘凡 | 基于多迁移学习模型融合的大坝裂缝检测方法 |
CN111739052A (zh) * | 2020-06-19 | 2020-10-02 | 山东凯鑫宏业生物科技有限公司 | 应用于医疗的基于自适应轮廓模型肺部mri图像分割方法及mri设备 |
CN112259223A (zh) * | 2020-10-22 | 2021-01-22 | 河北工业大学 | 基于全视野数字切片的病人级别肿瘤智能诊断方法 |
CN113421187A (zh) * | 2021-06-10 | 2021-09-21 | 山东师范大学 | 一种超分辨率重建方法、系统、存储介质、设备 |
CN113674247A (zh) * | 2021-08-23 | 2021-11-19 | 河北工业大学 | 一种基于卷积神经网络的x射线焊缝缺陷检测方法 |
CN113822880A (zh) * | 2021-11-22 | 2021-12-21 | 中南大学 | 一种基于深度学习的裂缝识别方法 |
CN114623776A (zh) * | 2022-05-16 | 2022-06-14 | 四川省公路规划勘察设计研究院有限公司 | 基于隧道变形监测的隧道损伤预测方法 |
Non-Patent Citations (1)
Title |
---|
基于深度卷积神经网络融合模型的路面裂缝识别方法;孙朝云;马志丹;李伟;郝雪丽;申浩;;长安大学学报(自然科学版)(04);全文 * |
Also Published As
Publication number | Publication date |
---|---|
CN115330740A (zh) | 2022-11-11 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN113674247B (zh) | 一种基于卷积神经网络的x射线焊缝缺陷检测方法 | |
CN110503112B (zh) | 一种增强特征学习的小目标检测及识别方法 | |
CN111914720B (zh) | 一种输电线路绝缘子爆裂识别方法及装置 | |
CN110991444B (zh) | 面向复杂场景的车牌识别方法及装置 | |
CN111461213B (zh) | 一种目标检测模型的训练方法、目标快速检测方法 | |
CN107944450A (zh) | 一种车牌识别方法及装置 | |
CN111178451A (zh) | 一种基于YOLOv3网络的车牌检测方法 | |
CN113780211A (zh) | 一种基于改进型Yolov4-tiny的轻量级飞机检测方法 | |
CN113420643B (zh) | 基于深度可分离空洞卷积的轻量级水下目标检测方法 | |
CN111738206B (zh) | 基于CenterNet的用于无人机巡检的挖掘机检测方法 | |
CN114973002A (zh) | 一种基于改进的YOLOv5的麦穗检测方法 | |
CN112381060A (zh) | 一种基于深度学习的建筑物地震破坏等级分类方法 | |
CN112861646B (zh) | 复杂环境小目标识别场景下的卸油工安全帽级联检测方法 | |
CN115620180A (zh) | 一种基于改进YOLOv5的航拍图像目标检测方法 | |
CN111640116A (zh) | 基于深层卷积残差网络的航拍图建筑物分割方法及装置 | |
CN114565959A (zh) | 基于YOLO-SD-Tiny的目标检测方法及装置 | |
CN113076804A (zh) | 基于YOLOv4改进算法的目标检测方法、装置及系统 | |
CN116597326A (zh) | 一种基于改进YOLOv7算法的无人机航拍小目标检测方法 | |
CN115330740B (zh) | 一种基于mdcn的轻量化裂缝识别方法 | |
CN115424276B (zh) | 一种基于深度学习技术的船牌号检测方法 | |
CN109190451B (zh) | 基于lfp特征的遥感图像车辆检测方法 | |
CN116129234A (zh) | 一种基于注意力的4d毫米波雷达与视觉的融合方法 | |
CN116310323A (zh) | 一种飞机目标实例分割方法、系统和可读存储介质 | |
CN115457412A (zh) | 一种基于Faster-rIR7-EC的混凝土裂缝快速识别方法 | |
CN114708590A (zh) | 基于深度学习的复合固化土微观结构识别分析方法及系统 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
CB03 | Change of inventor or designer information |
Inventor after: Cao Maosen Inventor after: Fu Ronghua Inventor after: Wang Jie Inventor after: Qian Xiangdong Inventor after: Zhu Kai Inventor before: Cao Maosen Inventor before: Fu Ronghua Inventor before: Zhu Huaxin Inventor before: Wang Jie Inventor before: Qian Xiangdong Inventor before: Emil Manoch Inventor before: Sumara Dragoslav Inventor before: Zhu Kai |
|
CB03 | Change of inventor or designer information | ||
TR01 | Transfer of patent right |
Effective date of registration: 20240304 Address after: Xikang Road, Gulou District of Nanjing city of Jiangsu Province, No. 1 210098 Patentee after: HOHAI University Country or region after: China Patentee after: Jiangsu Dongjiao Intelligent Control Technology Group Co.,Ltd. Address before: 210000 No. 1 Xikang Road, Gulou District, Nanjing City, Jiangsu Province Patentee before: HOHAI University Country or region before: China Patentee before: Jiangsu Dongjiao Intelligent Control Technology Group Co.,Ltd. Patentee before: JIANGSU ZHONGJI ENGINEERING TECHNOLOGY RESEARCH Co.,Ltd. Patentee before: JSTI GROUP Co.,Ltd. |
|
TR01 | Transfer of patent right |