CN103207186A - Identification method for defect detection of automatic cloth inspecting machine and system thereof - Google Patents
Identification method for defect detection of automatic cloth inspecting machine and system thereof Download PDFInfo
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- CN103207186A CN103207186A CN2013101190511A CN201310119051A CN103207186A CN 103207186 A CN103207186 A CN 103207186A CN 2013101190511 A CN2013101190511 A CN 2013101190511A CN 201310119051 A CN201310119051 A CN 201310119051A CN 103207186 A CN103207186 A CN 103207186A
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Abstract
The invention provides an identification method for the defect detection of an automatic cloth inspecting machine. Firstly, cloth images are collected by a charge-coupled device linear camera; the normal cloth and the detect of cloth are precisely distinguished by detect recognition algorithm; images which are judged as cloth detect by the machine are firstly stored in a hard disc of an industrial personal computer of the cloth inspecting machine; then the information such as the defect images and the defect position is transmitted to a remote terminal in an office by the host machine through the network; the defects of the defect images are enabled to have better human eye resolution ratio by image enhancement algorithm; and the category of the defects is judged by cloth inspectors according to the processed defect images. The method disclosed by the invention can realize the accurate detection of the cloth defects and the accurate identification of the category and can perform correct cloth quality rating. In addition, the harm to eyes of the cloth inspectors caused by the cloth detection is greatly reduced. The development cost is lowered, the method is easy for textile enterprises to accept and the cloth inspecting efficiency is greatly increased.
Description
Technical field
The present invention relates to textile industry, especially relate to the recognition methods of a kind of automatic cloth inspecting machine defect detection and system thereof.
Background technology
In the textile production industry, the detection of fabric defects is the key link of fabric quality control with identification, has very important meaning.Traditional detection method is to rely on manually to carry out the cloth check.The perching workman directly adopts bore hole to judge whether to have fault on the fabric according to experience and fault is classified.But artificial perching is limit by the human eye physiological structure, can't realize detection continuous, at a high speed, and the detection speed of per minute is the highest to be no more than 15 meters, generally about 10 meters.Because fabric defect is tiny, and need manually to carry out the identification of fault at the fabric that moves, this can cause the loss height of fault on the one hand, bore hole perching for a long time simultaneously, very easily cause visual fatigue, and human eye caused very big injury, this tends to cause this post recruitment difficulty, can't guarantee labour's supply.In addition, owing to different detect workmans and have different perching experiences, and sense of responsibility all is not quite similar, therefore, even if same percher also often is subjected to individual factor etc. to influence the problem that causes cloth quality inspection instability, consistance difference.
At present, automatic cloth inspecting machine is focus and the difficult point of textile machine automated arm development, and relevant defect detection and sorting algorithm emerge in an endless stream.But great majority invention is at present just distinguished fault and normal this two class of cloth, perhaps a few classes is distinguished.But the automatic cloth inspecting machine of actual demand is can both classify to the common fault of kind more than 30, sometimes even need distinguish more than 60 kind of fault on the cloth.For example the visible fault of cotton grey fabric often can be up to 30～40 classes; In order to improve output, the detection speed of present automatic cloth inspecting machine is near 100 meters/minute simultaneously, and the real-time of this accuracy to the fault sorting algorithm, categorizing system response has all proposed unprecedented challenge.Although detecting fault, the automatic cloth inspecting machine that present each producer develops has higher recall rate, but compare with manual detection for the identification of fault type and still to have big gap, often be difficult to satisfy requirement of client, thereby formed the bottleneck of automatic cloth inspecting machine development, made the development of present automatic cloth inspecting machine stay cool substantially.
The severe situation that rises significantly in view of present cost of labor and the automation level of enterprises require the actual conditions that increase substantially, and the development of automatic cloth inspecting machine will have more urgency.
Summary of the invention
But comprehensive artificial perching occurs the fault omission easily is higher to the accuracy of fault classification, and machine to the loss of fault low but to the accuracy rate of the fault classification characteristics far away from human eye, the present invention is based on machine vision technique has designed and developed out a kind of fault and has detected recognition methods and system automatically, described system combines machine and detects and manual detection characteristics separately, the two advantage is organically blended, take full advantage of machine fault recall rate height, can carry out high speed, the characteristics of continuous detecting and the high advantage of human eye fault classification accuracy, not only can realize the high speed of fault, automatically detect, minimizing is to the injury of percher people's human eye, but also has fault recall rate and the high characteristics of classification accuracy.
Its technical scheme is as described below:
The recognition methods of a kind of automatic cloth inspecting machine defect detection comprises the following steps:
1) start cloth inspecting machine, open the lighting source that is arranged on scanning cloth below, the line by line scan cloth of running of the line-scan digital camera of scanning cloth top, and the cloth view data of gathering passed in the internal memory of industrial computer;
2) to the view data in the internal memory, utilize the fault recognizer to carry out the accurate differentiation of normal cloth and Fabric Defects Inspection, the image that detects fault is made fault detect the mark position, and will differentiate picture-storage for fabric defects in the hard disk of industrial computer;
3) by network fault picture and fault are detected mark position information and be transferred to remote terminal, manually carry out the judgement of fault classification;
4) the Fabric Defects Inspection image is analyzed, to Fabric Defects Inspection name, the information of cloth is saved to the database of remote terminal, be used for inquiry and produce the cloth form.
Step 2) in, in the industrial computer internal memory, represents that with numeral 1 fault detects the mark position.Differentiation is the picture of fabric defects in the hard disk of described industrial computer, identifies fault with numeral 0 and detects the position.
In the step 3), before the artificial judgment, described fault picture makes fault have better resolution of eye by algorithm for image enhancement.
In the step 4), the analysis of Fabric Defects Inspection comprised the size of fault in the image is assessed, and according to the fault classification information this fault is given a mark.
Described Fabric Defects Inspection name ID is made up of the machine number of cloth inspecting machine, order number, sequence number and positional information four parts of cloth, and it is inferior that described sequence number comprises perching time and cloth, and described positional information comprises fault broadwise positional information and warp-wise positional information.
Described warp-wise positional information is calculated by camera resolution and cloth broadwise width information, and described broadwise positional information calculates by umber of pulse and the cloth gait of march that rotary encoder provides.
A kind of automatic cloth inspecting machine defect detection recognition system, comprise line-scan digital camera, the lighting source that is arranged on scanning cloth bottom, the industrial computer of preserving the camera image data that is arranged on scanning cloth top and is used for scanning cloth, the remote terminal that is used for the cloth data analysis, described line-scan digital camera links to each other with the image pick-up card of industrial computer by the Cameralink interface, and described industrial computer and remote terminal pass through network connection.
Described system gives full play to people's experience advantage and machine fast detecting characteristics, compare the injury that has reduced human eye with traditional cloth inspection method, reduce the perching working strength of workers, can greatly improve perching efficient, and make percher people work the place by polluting the office that more serious workshop is changed into totally, become clear.In addition, compare with present automatic cloth inspecting machine, because the borrower's of this system experience, it is not high to have overcome present automatic cloth inspecting machine fault classification accuracy, is difficult to all faults problem such as classify.
Description of drawings
Fig. 1 is the synoptic diagram of automatic cloth inspecting machine defect detection recognition system;
Fig. 2 is the schematic flow sheet of automatic cloth inspecting machine defect detection recognition methods;
Fig. 3 is the regular synoptic diagram of fault picture name ID;
Each component names is as described below: 1-cloth; The 2-line-scan digital camera; The 3-industrial computer; The 4-remote terminal; The 5-light source.
Embodiment
Native system adopts the CCD line-scan digital camera, be to be that Machine Design mode in the CN101851849A automatic cloth inspecting machine is come the linear array CCD camera setting according to the patent No., this system as shown in Figure 1, comprise line-scan digital camera 1, the lighting source 5 that is arranged on scanning cloth bottom, the industrial computer 3 of preserving the camera image data that is arranged on scanning cloth top and is used for scanning cloth, the remote terminal 4 that is used for the cloth data analysis, described line-scan digital camera 1 links to each other with the image pick-up card of industrial computer 3 by the Cameralink interface, and described industrial computer 3 passes through network connection with remote terminal 4.
As shown in Figure 2, at first CCD line-scan digital camera 1 is carried out initialization, acquisition parameter is set.Then, start cloth inspecting machine, allow cloth turn round.The cloth that line-scan digital camera 1 is lined by line scan and turned round, the cloth view data of Cai Jiing passes to industrial computer 3 internal memories thereupon, and the defect detection algorithm moves simultaneously, if the fault of detecting, fault detects the mark position, represents with numeral 1.The image that CCD transmits, is stored into the view data of record and positional information etc. on the hard disk of industrial computer 3 till scan line does not have fault by the consistent internal memory that is saved to thereupon, identifies fault with numeral 0 and detects the position.
Because this process is only distinguished normal cloth and fault, therefore detect the accuracy rate height, detection speed is fast.This process does not need people's participation, therefore can not damage by the eyes to the perching workman as the artificial cloth inspection method of tradition.Differentiated for after the picture of fabric defects is stored in the hard disk of cloth inspecting machine industrial computer 3 by machine, again fault picture and relevant defect position information are transferred to remote terminal 4 in the office via network, carry out image enhancement processing before checking the fault picture being shown to terminal perching personnel, make fault have better resolution of eye by algorithm for image enhancement, in order to make that the fault in the fault picture is more obvious.After this, the perching personnel at terminal operation just can carry out the fault classification to the fault picture after strengthening.
Search relevant fault picture for the ease of operating personnel, set naming rule and make the fault picture have unique ID at memory device.Store the name of fault picture on the industrial computer into and should follow as shown in Figure 3 nomenclature principle, it is the machine number of cloth inspecting machine, order number, sequence number and the positional information of cloth that the fault Image ID mainly is made up of four parts.Wherein, sequence number has comprised perching time and cloth time, and positional information has comprised broadwise positional information and warp-wise positional information.Warp-wise positional information X is calculated by CCD camera resolution and cloth broadwise width information, and broadwise positional information Y then calculates by umber of pulse and the cloth gait of march that rotary encoder provides.
The technology of the application of the invention, that has not only realized Fabric Defects Inspection accurately detects accurate identification with classification, carries out that correct cloth quality is commented etc., greatly reduces the cloth check simultaneously to the injury of percher's eye.Generally, the fault of cloth is less, and normal cloth accounts for the overwhelming majority, and the machine part is only identified normal cloth and fault, has reduced cost of development, is easier to textile enterprise accept, and can improve perching efficient greatly.
Claims (8)
1. automatic cloth inspecting machine defect detection recognition methods comprises the following steps:
1) start cloth inspecting machine, open the lighting source that is arranged on scanning cloth below, the line by line scan cloth of running of the line-scan digital camera of scanning cloth top, and the cloth view data of gathering passed in the internal memory of industrial computer;
2) to the view data in the internal memory, utilize the fault recognizer to carry out the accurate differentiation of normal cloth and Fabric Defects Inspection, the image that detects fault is made fault detect the mark position, and will differentiate picture-storage for fabric defects in the hard disk of industrial computer;
3) by network fault picture and fault are detected mark position information and be transferred to remote terminal, manually carry out the judgement of fault classification;
4) the Fabric Defects Inspection image is analyzed, to Fabric Defects Inspection name, the information of cloth is saved to the database of remote terminal, be used for inquiry and produce the cloth form.
2. according to the recognition methods of the described automatic cloth inspecting machine defect detection of claim 1, it is characterized in that: step 2) in, in the industrial computer internal memory, represent that with numeral 1 fault detects the mark position.
3. according to the recognition methods of the described automatic cloth inspecting machine defect detection of claim 1, it is characterized in that: step 2) in, differentiation is the picture of fabric defects in the hard disk of described industrial computer, identifies fault with numeral 0 and detects the position.
4. according to the recognition methods of the described automatic cloth inspecting machine defect detection of claim 1, it is characterized in that: in the step 3), before the artificial judgment, described fault picture makes fault have better resolution of eye by algorithm for image enhancement.
5. according to the recognition methods of the described automatic cloth inspecting machine defect detection of claim 1, it is characterized in that: in the step 4), the analysis of Fabric Defects Inspection comprised the size of fault in the image is assessed, and according to the fault classification information this fault is given a mark.
6. according to the recognition methods of the described automatic cloth inspecting machine defect detection of claim 1, it is characterized in that: in the step 4), described Fabric Defects Inspection name ID is made up of the machine number of cloth inspecting machine, order number, sequence number and positional information four parts of cloth, described sequence number comprises perching time and cloth time, and described positional information comprises fault broadwise positional information and warp-wise positional information.
7. according to the recognition methods of the described automatic cloth inspecting machine defect detection of claim 6, it is characterized in that: described warp-wise positional information is calculated by camera resolution and cloth broadwise width information, and described broadwise positional information calculates by umber of pulse and the cloth gait of march that rotary encoder provides.
8. automatic cloth inspecting machine defect detection recognition system, it is characterized in that: comprise line-scan digital camera, the lighting source that is arranged on scanning cloth bottom, the industrial computer of preserving the camera image data that is arranged on scanning cloth top and is used for scanning cloth, the remote terminal that is used for the cloth data analysis, described line-scan digital camera links to each other with the image pick-up card of industrial computer by the Cameralink interface, and described industrial computer and remote terminal pass through network connection.
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Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105261003A (en) * | 2015-09-10 | 2016-01-20 | 西安工程大学 | Defect point detection method on basis of self structure of fabric |
| CN105466938A (en) * | 2015-05-05 | 2016-04-06 | 北京经纬纺机新技术有限公司 | C/S structure-based fabric defect detection information management system and method thereof |
| CN106372696A (en) * | 2016-11-08 | 2017-02-01 | 苏州巨细信息科技有限公司 | Fabric inspection and defect data statistics system |
| CN106841222A (en) * | 2016-12-29 | 2017-06-13 | 贵州顺立达纺织科技有限公司 | A kind of image detecting system for automatic cloth inspection |
| CN107843741A (en) * | 2017-12-13 | 2018-03-27 | 中国地质大学（武汉） | A kind of cloth movement velocity measurement apparatus and method based on line array CCD |
| CN108931531A (en) * | 2017-05-24 | 2018-12-04 | 香港纺织及成衣研发中心 | A kind of Automatic Detection of Fabric Defects method, system and computer readable storage medium |
| CN109035248A (en) * | 2018-09-05 | 2018-12-18 | 深圳灵图慧视科技有限公司 | Defect detection method, apparatus, terminal device, server and storage medium |
| CN109215022A (en) * | 2018-09-05 | 2019-01-15 | 深圳灵图慧视科技有限公司 | Cloth inspection method, device, terminal device, server, storage medium and system |
| CN109385876A (en) * | 2018-10-23 | 2019-02-26 | 江南大学 | A kind of the intelligent quality management system and its method of woven fabric |
| CN110006908A (en) * | 2019-04-22 | 2019-07-12 | 东华大学 | A kind of image capturing system applying to cloth inspecting machine |
| CN110389130A (en) * | 2019-07-04 | 2019-10-29 | 盎古(上海)科技有限公司 | Intelligent checking system applied to fabric |
| CN110987968A (en) * | 2019-09-30 | 2020-04-10 | 烟台南山学院 | Method for detecting defects on surface of cloth by using three-dimensional matrix representation image |
| WO2020107744A1 (en) * | 2018-11-30 | 2020-06-04 | 深圳灵图慧视科技有限公司 | Fabric detection recording method and apparatus, device, and storage medium |
| CN111721773A (en) * | 2020-06-29 | 2020-09-29 | 北京大简技术有限公司 | Cloth detection system and method |
| CN112215824A (en) * | 2020-10-16 | 2021-01-12 | 南通大学 | YOLO-v 3-based cloth cover defect detection and auxiliary device and method |
| CN114921941A (en) * | 2022-03-24 | 2022-08-19 | 绍兴勇舜印染有限公司 | Dyeing and finishing processing method of all-cotton plain fabric |
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Cited By (22)
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| CN105466938B (en) * | 2015-05-05 | 2018-12-07 | 北京经纬纺机新技术有限公司 | A kind of fabric defects detection information management system and method based on C/S structure |
| CN105466938A (en) * | 2015-05-05 | 2016-04-06 | 北京经纬纺机新技术有限公司 | C/S structure-based fabric defect detection information management system and method thereof |
| CN105261003A (en) * | 2015-09-10 | 2016-01-20 | 西安工程大学 | Defect point detection method on basis of self structure of fabric |
| CN106372696A (en) * | 2016-11-08 | 2017-02-01 | 苏州巨细信息科技有限公司 | Fabric inspection and defect data statistics system |
| CN106841222A (en) * | 2016-12-29 | 2017-06-13 | 贵州顺立达纺织科技有限公司 | A kind of image detecting system for automatic cloth inspection |
| CN108931531A (en) * | 2017-05-24 | 2018-12-04 | 香港纺织及成衣研发中心 | A kind of Automatic Detection of Fabric Defects method, system and computer readable storage medium |
| US10942133B2 (en) | 2017-05-24 | 2021-03-09 | The Hong Kong Research Institute Of Textiles And Apparel Limited | Method and system for automatically detecting fabric defect, and computer readable storage medium |
| CN108931531B (en) * | 2017-05-24 | 2021-10-22 | 香港纺织及成衣研发中心 | Method, system and computer readable storage medium for automatically detecting fabric defects |
| CN107843741A (en) * | 2017-12-13 | 2018-03-27 | 中国地质大学（武汉） | A kind of cloth movement velocity measurement apparatus and method based on line array CCD |
| CN107843741B (en) * | 2017-12-13 | 2023-05-26 | 中国地质大学（武汉） | Cloth movement speed measuring device and method based on linear array CCD |
| CN109035248A (en) * | 2018-09-05 | 2018-12-18 | 深圳灵图慧视科技有限公司 | Defect detection method, apparatus, terminal device, server and storage medium |
| CN109215022A (en) * | 2018-09-05 | 2019-01-15 | 深圳灵图慧视科技有限公司 | Cloth inspection method, device, terminal device, server, storage medium and system |
| CN109385876A (en) * | 2018-10-23 | 2019-02-26 | 江南大学 | A kind of the intelligent quality management system and its method of woven fabric |
| WO2020107744A1 (en) * | 2018-11-30 | 2020-06-04 | 深圳灵图慧视科技有限公司 | Fabric detection recording method and apparatus, device, and storage medium |
| US20210279372A1 (en) * | 2018-11-30 | 2021-09-09 | Shenzhen Lintsense Technology Company Limited | Fabric detecting and recording method and apparatus |
| CN110006908A (en) * | 2019-04-22 | 2019-07-12 | 东华大学 | A kind of image capturing system applying to cloth inspecting machine |
| CN110389130A (en) * | 2019-07-04 | 2019-10-29 | 盎古(上海)科技有限公司 | Intelligent checking system applied to fabric |
| CN110987968A (en) * | 2019-09-30 | 2020-04-10 | 烟台南山学院 | Method for detecting defects on surface of cloth by using three-dimensional matrix representation image |
| CN111721773A (en) * | 2020-06-29 | 2020-09-29 | 北京大简技术有限公司 | Cloth detection system and method |
| CN112215824A (en) * | 2020-10-16 | 2021-01-12 | 南通大学 | YOLO-v 3-based cloth cover defect detection and auxiliary device and method |
| CN114921941A (en) * | 2022-03-24 | 2022-08-19 | 绍兴勇舜印染有限公司 | Dyeing and finishing processing method of all-cotton plain fabric |
| CN114921941B (en) * | 2022-03-24 | 2023-09-01 | 绍兴勇舜印染有限公司 | Dyeing and finishing processing method of all-cotton plain-color fabric |
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Application publication date: 20130717 |