تشخیص نواحی معیوب پارچه مبتنی برخوشه‌بندی و عملگرهای ریخت شناسی
محورهای موضوعی : انرژی های تجدیدپذیر
اکرم محمدی سومار
1
,
مهران عمادی
2
1 - گروه مهندسی برق- واحد مبارکه، دانشگاه آزاد اسلامی، مبارکه، اصفهان، ایران
2 - گروه مهندسی برق- واحد مبارکه، دانشگاه آزاد اسلامی، مبارکه، اصفهان، ایران
کلید واژه: پارچه معیوب, ناحیه بندی, عملگرهای ریخت شناسی, کانتورفعال,
چکیده مقاله :
در مراحل مختلف تولید پارچه، خرابی‌هایی متعددی برسطح پارچه ظاهر می‌شود. با چشم پوشی از دلایل ایجاد خرابی ها، تشخیص دقیق انواع آن‌ها به طبقه بندی صحیح پارچه کمک می‌کند و در نتیجه درصد بالایی از فرآیند کنترل کیفیت را به خود اختصاص می‌دهد. کنترل کیفیت پارچه به منظور بهبود کیفیت محصول و حفظ بازار رقابتی از اهمیت بالایی برخوردار است. همچنین شناسایی نواحی معیوب در روش های خودکار از اهمیت ویژه ای برخورداراست. در این مقاله، یک روش جدید، جهت ناحیه بندی نواحی معیوب پارچه ، مبتنی بر خوشه بندی و همچنین عملگرهای ریخت شناسی ارائه شده است. در روش پیشنهادی، پس از پیش پردازش های لازم جهت بهبود کیفیت تصویر، در مرحله اول روی تصویر خوشه بندی صورت می‌گیرد، تا نواحی مشابه ایجاد شوند. سپس عملگرهای ریخت شناسی به کار گرفته می شوند تا ناحیه معیوب استخراج شود. استفاده از ترکیب هوشمندانه عملگرهای ریخت شناسی، سبب شناسایی دقیق نواحی معیوب درتصویر پارچه شده است. نمایش ناحیه معیوب به کمک الگوریتم کانتور فعال صورت می گیرد. اگرچه تاکنون روش های متعددی همچون الگوهای محلی دودویی و سایر روش ها ارائه شده است، اما سرعت شناسایی این الگوریتم ها پایین بوده و پیچیدگی محاسباتی بالایی دارند. روش پیشنهادی روی پایگاه داده CMUPIE، پیاده سازی شده و به کمک معیارهای صحت و دقت ارزیابی شده است. صحت شناسایی نواحی معیوب در روش پیشنهادی، 82/93درصد و دقت روش پیشنهاد شده، 33/98 درصد حاصل گردیده است که در مقایسه با روش های مشابه، بهبود چشم گیری داشته است
At various stages of fabric production, there are numerous damages to the surface of the fabric. Regardless of the causes of the failures, precise identification of their types helps to correctly classify the fabric and thus provides a high percentage of the quality control process. Quality control of fabrics is of great importance in order to improve product quality and maintain a competitive market. Identification of faulty areas in automated methods is of great importance. In this paper, a new method is presented in the clustering of faulty zones based on clustering as well as morphological operators. In the proposed method, after preprocessing necessary to improve image quality, the first step is to cluster the image to create similar areas. Then morphological operators are applied to extract the defective area. The defective area is represented by the active contour algorithm. Although many methods such as local binary patterns and other methods have been proposed, the speed of detection of these algorithms is low and has high computational complexity. The proposed method is implemented on the CMUPIE database and evaluated using accuracy assessment criteria and accuracy criteria. The accuracy of identifying defective areas in the proposed method is 93.82%, and the precision of detecting defective areas in the suggested method is 98.33% which are significantly improved compared to similar methods.
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