Fen Bilimleri Enstitüsü

Permanent URI for this communityhttps://hdl.handle.net/20.500.12416/30

Browse

Browsing Fen Bilimleri Enstitüsü by Author "Abdilatef, Muhamad Azhar"

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • Item
    Citation Count: ABDİLATEF, M.A. (2014). Moving object detection in industrial line application. Yayımlanmamış yüksek lisans tezi. Ankara: Çankaya Üniversitesi Fen Bilimleri Enstitüsü.
    Moving object detection in industrial line application
    (2014-06) Abdilatef, Muhamad Azhar; Çankaya Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Bölümü
    In this thesis, a comparison study of moving object detection methods in industrial line application is presented. This comparison includes the consuming times, and the detection accuracy. According to consuming time, the methods are sorted into three groups (A, B, C). Groups A and B are including the methods those consuming a large time, thus they didn’t used for our application, while group C includes the methods with low consuming time. According to the detection accuracy, group C methods are compared one with each other to select the best method. The Misclassification rate (MR) is used to do this comparison. Some applied methods gave good results in detection but with high consuming time, others have problems in detection but with low consuming time. In this thesis a new method is presented using a combination between method 5 (the statistical morphological operation with the minimal bounding box) and method 6 (canny edge detection), this presented method classified as group C method, it did well in detection and consuming low time, thus it is used in the application of our thesis. The application set of our thesis consists of a prototype conveyer belt derived by a servo motor implementedespecially for our work, robot arm type Rios, and a stationary camera mounted on the top of the conveyer built. In our application five random types of objects are used, the location, size (area in pixels), and orientation for the region of interest (the objects) are detected depending on the images captured by the camera. When the object enters camera's scope, a captured image enters an image processing operation to detect the object, remark its features, and use it to make a robot arm go to the correct location to reach the object there, grip the object and move it to another location. Due to the MR results, it is obvious to notice that method 7 (Statistical Morphological Operation with MBR and Edges Detection) is gave the best results in the detection for all the used objects, according to that, method 7 is used in the implementation of the thesis application.