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    Computer aided control of cutting error in textile products
    (Ege Universitesi, 2017) Çevik, Kerim Kürsat; Koçer, Hasan Erdinç
    At present, the audits about the cutting error of textile products (leather, fabric, etc.) are made by the human by the eye via the template. Making these audits that necessitate accurate measurement by eye both takes so much time and enhance the risk occurrence risk. In this article, the image processing based industrial quality control system that determines the cutting errors of textile products automatically and discriminates between faulty and faultless products is explained. The system minimizes the faults based upon the human auditing and increases the number of pieces that are controlled by the unit of time. The performed system is composed of Panel PC, line scan camera, system of conveyor, basket control unit, image processing software and control user interface. The textile pieces (cuts) to be inspected come into the part by the conveyor where the camera and illumination unit are available, and the image is captured. This captured image is sent to the Panel PC and controlled whether there is a cutting error via image processing software. According to the result of the audit, the basket system at the end of the conveyor (conveyor belt) moves back and forth on wheel rail, and the textile pieces are provided to fall into the required basket. The performed system was tested on the leather pieces that were taken from a company in the leather sector. Totally it was tried by 150 times for 50 pieces of leather in 5 different templates and these pieces felt into the required basket correctly by discriminating for faulty/faultiness ones by 149 times (99,33% success ratio).
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    Computer-Assisted Automatic Egg Fertility Control
    (2019) Boğa, Mustafa; Çevik, Kerim Kürşat; Koçer, Hasan Erdinç; Burgut, Aykut
    This research aimed to determine the fertilization control of the eggs in an incubator between 0th and 5th days by image processing techniques via low-priced tools. Three different datasets that were composed of eggs whose images taken at different times in the incubator were prepared. Several filtering and morphology methods, gray level conversion and dynamic thresholding were utilized to process the 15 egg images. Moreover, the original processing codes based on the problem were given. White and Black percentages of binary images were utilized to determine the egg control. According to the test results, for the first dataset; 73.34% of fertility accuracy was achieved on the third day; 100% of fertility accuracy was achieved on the fourth day, for the second dataset; 93.34% of fertility accuracy was achieved on the third day; 93.34% of fertility accuracy was achieved again on the fourth day; for the third dataset, 93.34% of fertility accuracy was achieved on the third day; 100% of fertility accuracy again was achieved on the fourth day. When the results were evaluated, it was seen that egg fertility has been determined successfully automated with low cost tools.