پایش هجوم ماسه‌های بادی بر زیر ساخت‌های ریلی با استفاده از شبکه عصبی همگشتی و تصاویر پهپادی

نوع مقاله : علمی - پژوهشی

نویسندگان

1 کارشناسی‌ارشد، دانشکده مهندسی عمران، پردیس دانشکدگان فنی، دانشگاه تهران، ایران

2 استادیار، دانشکده مهندسی عمران، پردیس دانشکدگان فنی، دانشگاه تهران، ایران

3 دانشیار، عضو هیأت علمی دانشکده مهندسی راه‌آهن، دانشگاه علم و صنعت ایران، ایران

چکیده

خطوط ریلی در مناطق بیابانی با مشکلی جدی هجوم ماسه‌های بادی و انباشت آن‌ها بر روی ریل‌ها روبرو است. در اثر این پدیده، احتمال بسته شدن خطوط آهن، توقف حرکت قطارها و وقوع حادثه وجود دارد. در حال حاضر تلاش می شود تا با بازدیدهای میدانی در مناطق در معرض هجوم ماسه‌های بادی، از بروز این مشکل جلوگیری شود اما روش‌های سنتی با وجود ساده بودن دارای محدودیت‌هایی است که مهم‌ترین آنها هزینۀ مالی و زمانی بالای آن‌ها است. از این رو نیاز به بازنگری در روش‌های سنتی پایش در این مناطق به شدت احساس می‌شود. در این پژوهش با بکارگیری فناوری تصویربرداری پهپادی و پردازش هوشمند تصویر مبتنی بر شبکه‌های عصبی همگشتی و یادگیری عمیق یک سامانۀ جامع پایش طراحی شده است. استفاده از پهپادها و عمودپروازها در مدیریت و تعمیر و نگهداری و تعمیرات فراسازه‌های ترابری می‌تواند گام مهمی در بالا بردن دقت و سرعت و البته کاهش هزینه‌ها باشد. به این ترتیب پایش سامانۀ ریلی به جای آنکه تنها محدود به بازه‌های زمانی بازدید میدانی باشد، به صورت پیوسته زیر نظر است. در این تحقیق از شبکه‌ عصبی یولو با هدف تشخیص هجوم‌ ماسه‌های بادی بر شبکه ریلی در تصاویر پهپادی استفاده شده است. در نهایت با آموزش شبکه، دقت شبکه در تشخیص هجوم ماسه‌ بر سطح ریل 91 درصد بدست آمده است.

کلیدواژه‌ها


عنوان مقاله [English]

Monitoring Windblown Sand along Railway Infrastructures Using Automated Processing of Drone Imagery

نویسندگان [English]

  • Mohammad Safaei 1
  • Mahdi Samadzad 2
  • Morteza Bagheri 3
1 School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran
2 School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran
3 School of Railway Engineering, Iran University of Science and Technology, Tehran, Iran
چکیده [English]

Automated image processing technologies are being implemented increasingly for infrastructure monitoring applications thanks to their high level of accuracy, versatility and efficiency. Therefore, they can be employed for management, repair, and maintenance of transport meta-structures to reduce costs, enhance accuracy, and accelerate task completion. Moreover, the novel technology of unmanned aerial vehicles (UAVs) had a considerable effect on the improvement of different industries. This study addresses the influx of sand to the rail surface in desert areas. This problems is now analyzed through the conventional method (i.e. field observation), which is the simplest possible technique requiring ongoing inspection and large amounts of time and resources. Hence, the use of novel technologies of image processing and artificial intelligence as well as aerial images helped take a major step in the automated analysis of this problem. In rail networks across desert areas, the influx and accumulation of sand block railways, slow down and stop trains, and sometimes cause accidents. Thus, it is essential to adopt an automated system for the timely detection and resolution of this problems in order to prevent additional costs or further accidents. UAV-based imaging, smart image processing based on convolutional neural networks, and deep learning are employed in this study to design a comprehensive system for monitoring the problems. This system can greatly decrease the costs of monitoring the safety of rail networks and significantly improve the accuracy and efficiency of repair and maintenance systems. The proposed system can also provide the centralized ongoing monitoring of Iran’s transport network.

کلیدواژه‌ها [English]

  • Image processing
  • wind-blown sand
  • railway networks
  • convolutional neural network (CNN)
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