دوره 21، شماره 3 - ( مجله مهندسی برق و الکترونیک ایران - جلد 21 شماره 3 1403 )
جلد 21 شماره 3 صفحات 154-139 |
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Aghayan Mashhady N, Amirkhani A. Improving the Accuracy of Road Damage Detectors Using Traditional Augmentations and Object Bounding Box Augmentation. Journal of Iranian Association of Electrical and Electronics Engineers 2024; 21 (3) :139-154
URL: http://jiaeee.com/article-1-1561-fa.html
URL: http://jiaeee.com/article-1-1561-fa.html
اقایان مشهدی نیما، امیرخانی عبدالله. بهبود دقت شناساگرهای خرابیهای جاده با استفاده از داده افزاییهای سنتی و داده افزایی جعبه مرزی اشیاء. نشریه مهندسی برق و الکترونیک ایران. 1403; 21 (3) :139-154
دانشکده مهندسی خودرو- گروه برق و الکترونیک- دانشگاه علم و صنعت ایران
چکیده: (630 مشاهده)
شخیص خودکار خرابیهای جادهها به فرایند تعمیر و نگهداری از آن¬ها سرعت میبخشد و از تصادفات رانندگی بسیاری جلوگیری مینماید. در این مقاله برای بررسی عملکرد شناساگرهای خرابی جاده با استفاده از YOLOv5، ده شناساگر در پایگاه داده RDD2020 توسعه داده شد. با شبیه‌سازی شرایط محیطی مانند تابش شدید نور خورشید و سایه بر سطح جاده مشخص گردید شناساگرهای خرابی جاده در این شرایط عملکرد خوبی ندارند. در ادامه با استفاده از تکنیکهای داده افزایی سنتی، استحکام شناساگرهای خرابی جاده در شرایط محیطی مختلف بهبود داده شده است. با استفاده از این تکنیکها یک پایگاه صحت سنجی برای بررسی عملکرد شناساگرها در شرایط محیطی مختلف در پایگاه داده RDD2020 ایجاد گردید. به منظور حل چالش کمبود داده در این پایگاه از تکنیک داده افزایی جعبه مرزی و ترکیب آن با تلفیق پوآسن اصلاح‌شده بهره می بریم. در این روش تعداد نمونههای کلاس پایگاه داده مرجع در کلاس چاله و ترک افقی افزایش یافته است. نتایج این پژوهش نشان میدهد که آموزش شناساگرها با دادههای جدید، موجب بهبود عملکرد آنها در معیار F1-Score و mAP به ترتیب به میزان 33 و 50 درصد شده است.
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