Study On The Computer Vision Detect System Of Carded Web Quality

This research focused on the computer vision detecting system of the carded web quality on the cotton carding machine, including the detection and algorithm based on computer. Traditionally, the web quality is subjective visually evaluated by tester or operator, with low efficiency and long period. In this research, the web quality objective detect system is established and the corresponding algorithm is developed, which can evaluating the quality of carded web fast and conveniently, therefore, the efficiency and the degree of automatic in textile processing can be improved. In this research, the establishing of detect system, the pre-processing of detected image of carded web, the image segmentation and recognition on neps and trashes in web, and the calculation of evenness of the web and the resultant carded sliver are all dealt with. The contents of the dissertation are shown as follows:Chapter1is the general introduction and literatures review, the background of this research, the development of the visual detect system based on computer, and the previous research works focused on detecting on carded web, especially the research works in the detect on nep, trash in the web and the web evenness were reviewed and commented, some application of the detect and the devices were also introduced and commented.Chapter2focused on the establishing of computer vision detect system of carded web quality on a pilot carded machine. The construction and schematic of the system are introduced. The system is composed of three parts:image collection, image recognition processing and the operate interface of user. In the hardware of the system, the difference between linear array CCD and plane array CCD was compared and analyzed, therefore, the linear array CCD sensor was employed according to the requirement of this research, and the corresponding lens was selected. Then, some commonly used light sources were compared and analyzed, on the base of that, the LED with white line was selected as the light source of the system. Finally, a pair of assistant rollers was designed to amount in front of trumpet in card machine, which is helpful to avoid the crease of the web when it is condensed into a sliver. The parameters used in this system were introduced and optimized, the position of the CCD camera was determined on the width of the web and lens specification; the front light source was used in this system, after comparing the result between back light source and front light source; the speed of image acquisition was figured out on the base of web output speed; with a certain aperture, the relation between the intensity of illumination and the speed of image acquisition was obtained.Chapter3deals with the pre-processing of image. The collection of web image and its conversion into the discrete data image was introduced. With the reduce image method and top-hat transfer method, and consider the sequent trash detecting, the reduce image method combine average gray value of highest column on the background was used to overcome the unevenness of intensity of illumination; with the comparison of the reinforce result of correction of histogram and homomorphism filtering, the linear transfer of gray value was used to reinforce the image of web, which was convenient to sequent detect and nep recognition; with the comparison of some noise filtering and time consume, the Wiener filtering was employed to remove the noise in the web image.In Chapter4, the detecting of defaults in web was studied. The classification of web defaults, such as neps, trashes, cloud-like and broken part in web, were introduced. On the base of the characteristics of these defaults, the detecting method of the image was determined. The image segmented by gray threshold, and the size of flaws was detected by area threshold. For the detecting of nep, because the complex of texture, the global threshold was used to segment the image. By comparison the results of five methods used to segment the image, the Gauss fit threshold method was determined as the optimum method. For the detecting of trash and broken webs, the characteristic of their gray value correlation with that of background was used to segment the image. In this chapter, the cloud-like webs were detected by region growing method. Optimize the detecting process of the cotton web flaws, improving the detection speed and accuracy.The results of nep and trash recognition for300images compared to those of test results by people vision, the image recognition was much more faster with high correct detection rate and low false drop rate, which means the method used in this system is reasonable and liable. The test results were also compared with those of instrument, but because of the different mechanism, the detection of this system was different from those detected by AFIS.In chapter5, the calculation of the evenness of carded web was studied, therefore, the evenness of output sliver was figured out. On the base of the definition and characteristic of the evenness of web, the area gray value differential method and grey level co-occurrence matrix were used to calculate the evenness of the web, compare to the vision test on the web, this CV%value of area gray value differential method can be better to character the evenness of web, hence it not only can be used to evaluate the evenness of the web but also can be the standard level of evenness of the web. According to the web in trumpet, the relation between web and output sliver was established, therefore, the calculation of the evenness of output sliver by computer was proposed. The results calculated by computer and the results tested by YG135G instrument was linear related, that means the calculated results by computer can be used to present the evenness of sliver. In this chapter, the effects of speeds of both feed roller and doffer on the evenness of output sliver were also tested, the results showed that the calculated results meet the knowledge very well, it also means the method by this research was reasonable and feasible.Chapter6is the conclusion and prospect, the contents and results of this research was concluded, the deficiencies were also pointed out, the suggestion and for further research were proposed.Generally, this research established the computer vision detect system on carded web quality, the method to recognize the neps and trashes in web, the test was fast and conveniently and the results was much accuracy, which offers the base of online test and real-time control in card machine.