CN111738990A - LOG algorithm-based damaged fruit temperature field detection method - Google Patents
LOG algorithm-based damaged fruit temperature field detection method Download PDFInfo
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- CN111738990A CN111738990A CN202010497506.3A CN202010497506A CN111738990A CN 111738990 A CN111738990 A CN 111738990A CN 202010497506 A CN202010497506 A CN 202010497506A CN 111738990 A CN111738990 A CN 111738990A
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- 235000013399 edible fruits Nutrition 0.000 title claims abstract description 28
- 238000001514 detection method Methods 0.000 title claims abstract description 15
- 238000003708 edge detection Methods 0.000 claims abstract description 16
- 238000001914 filtration Methods 0.000 claims abstract description 13
- 230000000694 effects Effects 0.000 claims abstract description 6
- 235000002566 Capsicum Nutrition 0.000 claims abstract description 4
- 239000006002 Pepper Substances 0.000 claims abstract description 4
- 235000016761 Piper aduncum Nutrition 0.000 claims abstract description 4
- 235000017804 Piper guineense Nutrition 0.000 claims abstract description 4
- 235000008184 Piper nigrum Nutrition 0.000 claims abstract description 4
- 238000009499 grossing Methods 0.000 claims abstract description 4
- 150000003839 salts Chemical class 0.000 claims abstract description 4
- 244000203593 Piper nigrum Species 0.000 claims abstract 2
- 238000000034 method Methods 0.000 abstract description 5
- 230000007547 defect Effects 0.000 description 5
- 241001164374 Calyx Species 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 244000141359 Malus pumila Species 0.000 description 2
- 241000722363 Piper Species 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000009659 non-destructive testing Methods 0.000 description 2
- 238000001931 thermography Methods 0.000 description 2
- 235000021016 apples Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
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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
- G06T7/0004—Industrial image inspection
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N25/00—Investigating or analyzing materials by the use of thermal means
- G01N25/72—Investigating presence of flaws
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
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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
- G06T2207/10—Image acquisition modality
- G06T2207/10048—Infrared image
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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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30128—Food products
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Abstract
The invention discloses a LOG algorithm-based damaged fruit temperature field detection method, which comprises the following steps: s1, acquiring an infrared picture of the fruit by using a thermal infrared imager; s2 adding salt and pepper noise to the picture to obtain a noise picture; s3 generating different Gaussian templates by using Gaussian functions; s4, smoothing the picture by using different Gaussian templates to obtain different filtering effect graphs, and deepening the color depth of the damaged part of the fruit; s5, converting the filtering result image into a gray image; s6 uses LOG algorithm to carry out edge detection and obtain different edge detection results. The method compares the influence of different Gaussian templates on the detection of the damaged edge and obtains the clear outline of the damaged part.
Description
Technical Field
The invention relates to the technical field of nondestructive testing, in particular to a damaged fruit temperature field detection method based on an LOG algorithm.
Background
The nondestructive testing technology is a technology for measuring the quality of a measured object according to the characteristics of the measured object such as heat, light, electricity and the like on the premise of not damaging the measured object. The portal flood and the like research the temperature difference characteristic between the damaged part and the whole part of the apple, obtain a comparison thermal image by setting different heating distances and shooting distances, and qualitatively and quantitatively analyze the temperature curve and the temperature difference of the fruit stem and the calyx so as to eliminate the influence of the fruit stem and the calyx on the extraction of the damaged characteristic. The change of the surface temperature of the damaged apples is studied by utilizing a thermal imaging technology, and the result shows that the change of the temperature curve of the defect part is obviously different from the change of the temperature curve of the calyx of the fruit stem. From the above studies, it can be known that the thermal imaging technique can achieve the purpose of defect detection. However, the above studies have judged only the temperature difference characteristics of the damaged portion, and have not obtained the edge profile of the damaged region.
The edge profile of the damaged fruit can be obtained using edge detection techniques. Edge detection is a basic problem in image processing and computer vision, and has great significance for feature extraction and target identification in image processing. The fruit quality is greatly influenced once the fruit has defects. The defect detection of the fruit mainly aims at detecting the defects on the surface of the fruit in time. The method comprises the steps of firstly filtering an obtained thermal image to deepen the edge of a damaged part image, and then extracting the edge by using a LoG algorithm.
Disclosure of Invention
Based on the technical problems in the background art, the invention provides a damaged fruit temperature field detection method based on an LOG algorithm.
The invention provides a LOG algorithm-based damaged fruit temperature field detection method, which comprises the following steps:
s1, acquiring an infrared picture of the fruit by using a thermal infrared imager;
s2 adding salt and pepper noise to the picture to obtain a noise picture;
s3 generating different Gaussian templates by using Gaussian functions;
s4, smoothing the picture by using different Gaussian templates to obtain different filtering effect graphs, and deepening the color depth of the damaged part of the fruit;
s5, converting the filtering result image into a gray image;
s6 uses LOG algorithm to carry out edge detection and obtain different edge detection results.
Preferably, the LOG algorithm in step 6 is defined as:
according to the method for detecting the temperature field of the damaged fruit based on the LOG algorithm, the influence of different Gaussian templates on the detection of the damaged edge is compared, and the clear outline of the damaged part is obtained.
Drawings
FIG. 1 is a graph of the filtering results of different Gaussian templates of the temperature field detection method of damaged fruits based on LOG algorithm;
fig. 2 is a graph of different gaussian kernels and edge detection results of the damaged fruit temperature field detection method based on the LOG algorithm.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1-2, the damaged fruit temperature field detection method based on the LOG algorithm comprises the following steps:
s1, acquiring an infrared picture of the fruit by using a thermal infrared imager;
s2 adding salt and pepper noise to the picture to obtain a noise picture;
s3 generating different Gaussian templates by using Gaussian functions;
s4, smoothing the picture by using different Gaussian templates to obtain different filtering effect graphs, and deepening the color depth of the damaged part of the fruit;
s5, converting the filtering result image into a gray image;
s6 uses LOG algorithm to carry out edge detection and obtain different edge detection results.
In the present invention, the LOG algorithm in step 6 is defined as:
as shown in fig. 1, the filtering results of different gaussian templates are shown, where K represents the size of the gaussian kernel. From the figure, we can observe that a larger kernel size and sigma value have better processing effect on noise, and when K is 11, the edge shape of a damage can be better shown and deepened, which is beneficial to the next step of edge detection;
as shown in fig. 2, the edge detection result is shown, where K represents the size of the gaussian kernel, and it can be directly observed from the edge detection result that the filtering result is the worst when K is 3, and there is much noise, so that a great obstacle is generated when performing edge detection, and the damaged portion cannot be detected, and as the size of the gaussian kernel increases, the filtering effect gradually increases, and the noise point gradually disappears, but as the size of the gaussian kernel increases, the intact portion around the damaged portion is also considered as the damaged portion, for example, when K is 13, the complete portion around the damaged portion is detected as the damaged portion;
with the increasing value, the contour of the edge detection becomes clearer, and the contour feature of the damaged part can be directly observed, for example, when K is 11, the contour of the damaged part of the image is closest to the real damaged part.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (2)
1. A damaged fruit temperature field detection method based on an LOG algorithm is characterized by comprising the following steps:
s1, acquiring an infrared picture of the fruit by using a thermal infrared imager;
s2 adding salt and pepper noise to the picture to obtain a noise picture;
s3 generating different Gaussian templates by using Gaussian functions;
s4, smoothing the picture by using different Gaussian templates to obtain different filtering effect graphs, and deepening the color depth of the damaged part of the fruit;
s5, converting the filtering result image into a gray image;
s6 uses LOG algorithm to carry out edge detection and obtain different edge detection results.
2. The LOG algorithm based damaged fruit temperature field detection method according to claim 1, wherein the LOG algorithm in step 6 is defined as:
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| CN202010497506.3A CN111738990A (en) | 2020-06-03 | 2020-06-03 | LOG algorithm-based damaged fruit temperature field detection method |
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Citations (5)
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|---|---|---|---|---|
| JP2002098654A (en) * | 2000-09-22 | 2002-04-05 | Sumitomo Metal Mining Co Ltd | Method for judging inner quality of fruit and vegetable, and x-ray light path length measurement method used for the same |
| CN201575977U (en) * | 2009-09-30 | 2010-09-08 | 浙江大学 | Thermal infrared imaging detecting system for fruit surface damage |
| CN102521802A (en) * | 2011-11-28 | 2012-06-27 | 广东省科学院自动化工程研制中心 | Mathematical morphology and LoG operator combined edge detection algorithm |
| CN108510512A (en) * | 2017-05-17 | 2018-09-07 | 苏州纯青智能科技有限公司 | A kind of thermal infrared imager method for detecting image edge |
| US20190331301A1 (en) * | 2016-12-30 | 2019-10-31 | Du Yuchuan | Method for leakage detection of underground pipeline corridor based on dynamic infrared thermal image processing |
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2020
- 2020-06-03 CN CN202010497506.3A patent/CN111738990A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002098654A (en) * | 2000-09-22 | 2002-04-05 | Sumitomo Metal Mining Co Ltd | Method for judging inner quality of fruit and vegetable, and x-ray light path length measurement method used for the same |
| CN201575977U (en) * | 2009-09-30 | 2010-09-08 | 浙江大学 | Thermal infrared imaging detecting system for fruit surface damage |
| CN102521802A (en) * | 2011-11-28 | 2012-06-27 | 广东省科学院自动化工程研制中心 | Mathematical morphology and LoG operator combined edge detection algorithm |
| US20190331301A1 (en) * | 2016-12-30 | 2019-10-31 | Du Yuchuan | Method for leakage detection of underground pipeline corridor based on dynamic infrared thermal image processing |
| CN108510512A (en) * | 2017-05-17 | 2018-09-07 | 苏州纯青智能科技有限公司 | A kind of thermal infrared imager method for detecting image edge |
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| 田光兆、姬长英、王海青、冯良宝: "基于MATLAB的若干苹果边缘检测方法及其特性的对比研究", 《科学技术与工程》, pages 3873 - 3877 * |
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