LU500649B1 - Verfahren und system zur erkennung von fischnetzschäden durch unterwasserbildgebung basierend auf tiefenlernen - Google Patents
Verfahren und system zur erkennung von fischnetzschäden durch unterwasserbildgebung basierend auf tiefenlernen Download PDFInfo
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- LU500649B1 LU500649B1 LU500649A LU500649A LU500649B1 LU 500649 B1 LU500649 B1 LU 500649B1 LU 500649 A LU500649 A LU 500649A LU 500649 A LU500649 A LU 500649A LU 500649 B1 LU500649 B1 LU 500649B1
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- Luxembourg
- Prior art keywords
- fishnet
- damage
- image
- underwater imaging
- damage detection
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- 238000003384 imaging method Methods 0.000 title claims abstract description 97
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000001514 detection method Methods 0.000 claims abstract description 111
- 238000012549 training Methods 0.000 claims abstract description 18
- 238000012360 testing method Methods 0.000 claims abstract description 10
- 230000006870 function Effects 0.000 claims description 21
- 238000004891 communication Methods 0.000 claims description 20
- 238000013136 deep learning model Methods 0.000 claims description 6
- 240000007651 Rubus glaucus Species 0.000 claims description 3
- 235000011034 Rubus glaucus Nutrition 0.000 claims description 3
- 235000009122 Rubus idaeus Nutrition 0.000 claims description 3
- 230000009191 jumping Effects 0.000 claims description 2
- 238000009499 grossing Methods 0.000 claims 1
- 238000013527 convolutional neural network Methods 0.000 description 7
- 241000251468 Actinopterygii Species 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 230000011218 segmentation Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000009395 breeding Methods 0.000 description 1
- 230000001488 breeding effect Effects 0.000 description 1
- 239000000969 carrier Substances 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000003708 edge detection Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007500 overflow downdraw method Methods 0.000 description 1
- 238000000638 solvent extraction Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000004642 transportation engineering Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/05—Underwater scenes
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01K—ANIMAL HUSBANDRY; AVICULTURE; APICULTURE; PISCICULTURE; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
- A01K75/00—Accessories for fishing nets; Details of fishing nets, e.g. structure
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/55—Clustering; Classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- 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
- G06F18/24137—Distances to cluster centroïds
- G06F18/2414—Smoothing the distance, e.g. radial basis function networks [RBFN]
-
- 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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- 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
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- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Multimedia (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Library & Information Science (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Computation (AREA)
- Environmental Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Animal Husbandry (AREA)
- Marine Sciences & Fisheries (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Image Analysis (AREA)
- Farming Of Fish And Shellfish (AREA)
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011036004.7A CN112163517A (zh) | 2020-09-27 | 2020-09-27 | 一种基于深度学习的水下成像鱼网破损识别方法及系统 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| LU500649A1 LU500649A1 (de) | 2022-03-28 |
| LU500649B1 true LU500649B1 (de) | 2022-04-08 |
Family
ID=73861310
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| LU500649A LU500649B1 (de) | 2020-09-27 | 2020-12-28 | Verfahren und system zur erkennung von fischnetzschäden durch unterwasserbildgebung basierend auf tiefenlernen |
Country Status (3)
| Country | Link |
|---|---|
| CN (1) | CN112163517A (zh) |
| LU (1) | LU500649B1 (zh) |
| WO (1) | WO2022062242A1 (zh) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114758260B (zh) * | 2022-06-15 | 2022-10-18 | 成都鹏业软件股份有限公司 | 工地安全防护网检测方法及系统 |
| CN116228757B (zh) * | 2023-05-08 | 2023-08-29 | 山东省海洋科学研究院(青岛国家海洋科学研究中心) | 一种基于图像处理算法的深海网箱网衣检测方法 |
| CN117115688A (zh) * | 2023-08-17 | 2023-11-24 | 广东海洋大学 | 基于深度学习的低亮度环境下死鱼识别计数系统及方法 |
| CN117309900B (zh) * | 2023-09-25 | 2024-03-22 | 中国水产科学研究院南海水产研究所 | 一种浅海渔网破损检测装置及控制方法 |
| CN118283145A (zh) * | 2024-03-25 | 2024-07-02 | 亿海蓝（北京）数据技术股份公司 | 网位仪的识别方法、装置、可读存储介质和ais系统 |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108734117A (zh) * | 2018-05-09 | 2018-11-02 | 国网浙江省电力有限公司电力科学研究院 | 基于yolo的电缆设备外部腐蚀破损识别方法 |
| CN109409365A (zh) * | 2018-10-25 | 2019-03-01 | 江苏德劭信息科技有限公司 | 一种基于深度目标检测的待采摘水果识别和定位方法 |
| KR102234697B1 (ko) * | 2018-11-02 | 2021-04-02 | 광주과학기술원 | 수중드론을 이용하는 어망감시장치, 및 그 장치의 제어방법 |
| CN109886344A (zh) * | 2019-02-26 | 2019-06-14 | 广东工业大学 | 基于深度学习的皮革破损识别方法、系统及设备和介质 |
| CN110163798B (zh) * | 2019-04-18 | 2020-12-04 | 中国农业大学 | 渔场围网破损检测方法及系统 |
| CN110335245A (zh) * | 2019-05-21 | 2019-10-15 | 青岛科技大学 | 基于单目时空连续图像的网箱网衣破损监测方法及系统 |
| CN110223293A (zh) * | 2019-06-21 | 2019-09-10 | 中国神华能源股份有限公司 | 列车车体破损的智能识别方法及识别装置 |
| CN111583197B (zh) * | 2020-04-23 | 2022-05-13 | 浙江大学 | 结合SSD及Resnet50网络的电力箱图片锈蚀破损识别方法 |
-
2020
- 2020-09-27 CN CN202011036004.7A patent/CN112163517A/zh active Pending
- 2020-12-28 LU LU500649A patent/LU500649B1/de active IP Right Grant
- 2020-12-28 WO PCT/CN2020/140287 patent/WO2022062242A1/zh active Application Filing
Also Published As
| Publication number | Publication date |
|---|---|
| LU500649A1 (de) | 2022-03-28 |
| WO2022062242A1 (zh) | 2022-03-31 |
| CN112163517A (zh) | 2021-01-01 |
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| FG | Patent granted |
Effective date: 20220408 |