مجله ماشین بینایی و پردازش تصویر

مجله ماشین بینایی و پردازش تصویر

تشخیص نمادهای ناهنجار در داده‌های تصویری با استفاده از مدل‌های یادگیری عمیق

نوع مقاله : مقاله پژوهشی

نویسندگان
گروه مهندسی کامپیوتر، دانشکده برق و کامپیوتر، دانشگاه صنعتی همدان
چکیده
با گسترش روزافزون محتوای تصویری در فضای مجازی، نظارت دستی بر نمادهای ناهنجار عملاً غیرممکن شده و نیاز به سامانه‌های هوشمند تشخیص خودکار بیش از پیش احساس می‌شود. در این پژوهش، یک مجموعه‌داده اختصاصی شامل 3659 تصویر از سه نماد «چشم جهان‌بین»، «ستاره واژگون» و «گونیا و پرگار» گردآوری و به‌صورت دقیق برچسب‌گذاری شد. داده‌ها با نسبت 80٪ برای آموزش و 20٪ برای آزمون تقسیم گردیدند و به‌منظور افزایش تنوع بصری و بهبود تعمیم‌پذیری، تکنیک‌های افزایش داده بر مجموعه آموزشی اعمال شد. عملکرد مدل‌های Faster R-CNN، SSD300 و خانواده YOLO (نسخه‌های YOLOv8 تا YOLOv12) مورد ارزیابی و مقایسه قرار گرفت. نتایج نشان داد مدل Faster R-CNN در مجموعه آزمون به مقادیر Precision برابر با 0.901، Recall برابر با 0.909 و mAP50 برابر با 0.939 دست یافته و نسبت به مدل‌های تک‌مرحله‌ای عملکرد برتری ارائه کرده است. یافته‌ها حاکی از آن است که معماری‌های دو‌مرحله‌ای در تشخیص نمادهای دارای جزئیات هندسی ظریف و شباهت ساختاری بالا، از قابلیت تعمیم‌پذیری بهتری برخوردارند. نتایج این پژوهش می‌تواند مبنایی برای توسعه سامانه‌های هوشمند پایش و فیلترینگ خودکار محتوای تصویری در کاربردهای نظارتی و تحلیل رسانه‌ای باشد.
کلیدواژه‌ها

عنوان مقاله English

Detection of Anomalous Symbols in Image Data Using Deep Learning Models

نویسندگان English

Mahsa sirooziyan fard
mahlagha afrasiabi
Department of Computer Engineering, Hamedan University of Technology, Hamedan, IRAN
چکیده English

With the rapid growth of visual content in digital environments, manual monitoring of anomalous symbols has become practically infeasible. In this study, a dedicated dataset consisting of 3,659 images of three symbols, namely the All-Seeing Eye, the Inverted Star, and the Square and Compass, was collected and annotated. The dataset was divided into training and testing sets with an 80%–20% ratio. To increase visual diversity and improve model generalization, data augmentation techniques were applied to the training set. The performance of Faster R-CNN, SSD300, and the YOLO family (versions 8 to 12) was evaluated. The results demonstrated that Faster R-CNN achieved Precision of 0.901, Recall of 0.909, and mAP50 of 0.939 on the test set, outperforming the single-stage detection models. The findings indicate that two-stage detection models provide better generalization when dealing with symbols containing fine geometric details and high structural similarities. This research can serve as a foundation for developing intelligent systems for automated visual content monitoring in cultural security and media filtering applications.

کلیدواژه‌ها English

Anomalous symbols
Object detection models
Image dataset
YOLO
SSD
Faster R-CNN
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دوره 13، شماره 1
تابستان 1405
صفحه 45-58

  • تاریخ دریافت 12 آذر 1404
  • تاریخ بازنگری 28 خرداد 1405
  • تاریخ پذیرش 25 تیر 1405