تشخیص و دستهبندی ترک‌‌های روسازی با استفاده از شبکه‌های پیچشی عمیق

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده عمران و محیط زیست، دانشگاه صنعتی امیرکبیر

2 hafez

چکیده

ارزیابی اطلاعات روسازی یکی از مهم‌ترین گام های پیاده سازی سامانه مدیریت روسازی است و سالانه تلاش های گسترده‌ای به منظور افزایش کارایی این سامانه با استفاده از فناوری‌های جدید انجام شده است. در سال های اخیر تمرکز سازمان ها بر توسعه سامانه های خودکار به منظور برداشت و ارزیابی بهتر اطلاعات روسازی بوده و تحقیقات گسترده ای در این زمینه انجام شده است. دانش داده کاوی و یادگیری ماشین با هدف بهره گیری از داده‌های موجود برای ساخت سامانه‌های هوشمند از جمله جدیدترین زمینه های تحقیقاتی در علوم مختلف نظیر پزشکی، مهندسی، اقتصادی است و نتایج بسیار خوبی از به کارگیری این دانش‌ها بدست آمده است. در زمینه مدیریت روسازی تحقیقات متعددی با هدف به کارگیری یادگیری ماشین به ویژه در ارزیابی خرابی‌های روسازی انجام شده است و نتایج این تحقیقات نشان می‌دهد که روش های مبتنی بر داده کاوی و هوش مصنوعی، ابزار های قدرتمندی در ساخت سامانه‌های خودکار و هوشمند هستند. در این مقاله ضمن تشریح مفاهیم تئوری، تلاش شده است که مدل‌هایی با هدف تشخیص و دسته بندی خرابی ترک خوردگی روسازی با استفاده از شبکه‌های پیچشی عمیق و به کارگیری روش انتقال یادگیری ایجاد شود و عملکرد آن ها از نظر دقت و سرعت یادگیری و اجرا مورد ارزیابی قرار گیرد. نتایج این پژوهش نشان می‌دهد که سرعت عملکرد مدل‌ها تا حد زیادی به مشخصه‌های مدل‌های از پیش تعلیم یافته بستگی دارد و دقت مدل‌ها بر اساس معیارهای مختلف (F-score، sensitivity، accuracyو ...) در بازه 0/94 تا 0/99 است که بیانگر عملکرد خوب مدل‌های مبتنی بر شبکه‌های پیچشی عمیق در تشخیص و ارزیابی خرابی‌های روسازی نظیر ترک خوردگی است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Pavement cracks detection and classification using deep convolutional networks

نویسندگان [English]

  • sajad ranjbar 1
  • Fereydoon Moghaddasnezhad 1
  • HAMZEH ZAKERI 2
1 Department of Civil & Environmental Engineering, Amirkabir University of Technology
2 RESEARCHER /AUT
چکیده [English]

Pavement inspection is one of the most important steps in the implementation of the pavement management system and extend efforts have been conducted to increase the efficiency of this system by using new technologies. In recent years, transportation agencies focus on creating automatic and more efficient systems for pavement inspection and a large number of researches have been done for this aim. According to the progress of computer science, data mining and machine learning as computer-based methods are used more in various areas (such as engineering, medical and economy), and significant results have been achieved. In the pavement management area, several researches have been performed to apply the machine learning, especially in pavement distresses evaluation. In this paper, the theoretical concepts have been explained, and several models have been created based on deep convolutional networks using transfer learning to detect and classify pavement cracks as the most prevalent pavement distress, and the performance of these models has been evaluated considering learning and test speed, and accuracy as the most important performance parameters. The results of this research indicate that the speed of models almost depends on the characteristics of pre-trained models that applied in the transfer learning process. Also, the accuracy of models based on various metrics (Sensitivity, F-score, etc.) is in range of 0.94 to 0.99 and indicates that deep learning method can be used to create expert systems for detection, classification, and quantification of pavement distresses such as cracking.

کلیدواژه‌ها [English]

  • Deep learning
  • transfer learning
  • pavement cracking
  • Detection
  • Classification
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