جاده

جاده

پردازش تصویر دو‌بعدی برای شناسایی خودکار خرابی چاله‌ در روسازی آسفالتی: مقایسه رویکردهای ویژگی‌محور و یادگیری ماشین

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانش آموخته کارشناسی ارشد، گروه راه و ترابری، دانشکده مهندسی عمران، دانشگاه علم‌و‌صنعت ایران، تهران، ایران
2 دانشجوی دکتری، دانشکده مهندسی عمران، دانشگاه صنعتی شاهرود، سمنان، ایران
3 دانشیار، گروه راه و ترابری، دانشکده مهندسی عمران، دانشگاه علم‌و‌صنعت ایران، تهران، ایران
4 استاد، گروه راه و ترابری، دانشکده مهندسی عمران، دانشگاه علم‌و‌صنعت ایران، تهران، ایران
چکیده
شناسایی دقیق و به ‌موقع خرابی‌ها در روسازی آسفالتی، نقشی اساسی در ایمنی و پایداری عملکرد راه‌ها دارد. روش‌های سنتی مانند بازرسی چشمی، با محدودیت‌هایی نظیر خطای انسانی، هزینه بالا و عدم تکرارپذیری مواجه‌اند. در این پژوهش، با استفاده از پردازش تصویر دوبعدی، دو رویکرد متفاوت شامل روش ویژگی‌محور و یادگیری ماشین برای شناسایی خودکار خرابی چاله مورد بررسی و مقایسه قرار گرفته‌اند. ابتدا مجموعه‌ای از تصاویر دارا و فاقد چاله گردآوری و با اعمال مراحل پیش‌پردازش، ویژگی‌های مؤثر استخراج گردید. سپس الگوریتم‌های مختلف طبقه‌بندی شامل ماشین بردار پشتیبان (SVM)، K نزدیک‌ترین همسایه، درخت تصمیم و جنگل تصادفی آموزش داده شدند. نتایج تجربی نشان داد که الگوریتم SVM با کرنل خطی، با دقتی بیش از ۹۷ درصد، در تفکیک تصاویر دارای چاله عملکرد بهتری دارد. رویکرد پیشنهادی با وجود سادگی محاسباتی، توانسته است، دقت بالا و کارایی رضایت‌بخش در مقایسه با روش‌های سنتی، تحت شرایط نوری کنترل‌شده (هوای ابری) و زاویه تصویربرداری ثابت را فراهم آورد. این روش می‌تواند به‌عنوان جایگزینی عملی و کم‌هزینه برای بازرسی بصری در مدیریت نگهداری روسازی‌ها مورد استفاده قرار گیرد.
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