شناسایی و دسته بندی ترک‌های روسازی آسفالتی با کمک الگوریتم آشکارسازی YOLOv5

نوع مقاله : مقاله عمران

نویسندگان

1 گروه مهندسی عمران، دانشکده فنی و مهندسی، موسسه آموزش عالی اقبال لاهوری

2 گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، موسسه آموزش عالی اقبال لاهوری

چکیده

تشخیص خودکار ترک روسازی برای ارزیابی الزامات تعمیر و نگهداری راه و اطمینان از ایمنی رانندگی ضروری است. تشخیص سنتی ترک دارای مشکلاتی مانند بازدهی پایین و عدم شناسایی کامل است. این پژوهش باهدف رفع مشکلات روش‌های سنتی تشخیص ترک و استفاده از مدل‌های یادگیری عمیق، روشی مبتنی بر الگوریتم‌های آشکارسازی و تشخیص شی برای تشخیص ترک روسازی طراحی کرده و ضمن تشریح مفاهیم تئوری، آخرین مدل‌های تشخیص اشیا سری YOLOv5 را برای تشخیص ترک روسازی موردبحث قرار داده است. درنهایت یک مدل آشکارسازی ترک و مدیریت روسازی مؤثر ارائه شده است. این مدل قادر است نوع، موقعیت و مشخصات هندسی ترک را با دقت و سرعت بالایی نسبت به سایر روش‌ها مشخص کند. بدین منظور از تصاویر برداشت‌شده از آسفالت معابر شهر مشهد برای آموزش و ارزیابی مدل استفاده شد. تصاویر برای دو گزینه ترک خطی و ترک سطحی برچسب-گذاری شد. سپس مدل‌هایی با به‌کارگیری پنج الگوریتم سری YOLOv5 و یادگیری انتقالی، ایجاد و ازنظر دقت و سرعت پیش‌بینی مورد ارزیابی قرار گرفته است. دقت مدل‌ها بین ۷۷ تا ۹۸ درصد و سرعت پیش‌بینی مدل‌ها بین 4/17 تا 105 میلی‌ثانیه است که بیانگر عملکرد مطلوب مدل‌ها است. مدل v5s با داشتن دقت 8/92 درصد و سرعت 9/23 میلی‌ثانیه، به‌عنوان مدل نهایی جهت پیش‌بینی واقعی ترک در یکی از معابر اصلی شهر مشهد استفاده شد. با توجه‌ به ابعاد و نوع ترک پیش‌بینی‌شده و استفاده از درخت تصمیم پیشنهادی، رویکرد تعمیر و نگهداری برای هر قطعه مشخص گردید.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Detection and classification of asphalt pavement cracks using YOLOv5

نویسندگان [English]

  • hassan hosseinzadeh 1
  • Ali ghiami bajgirani 1
  • mohadeseh delavarian 2
1 Department of Civil Engineering, Faculty of Engineering, Eqbal Lahori Institute of Higher Education
2 Department of computer engineering,Faculty of engineering,Eqbal lahori institute of higher education
چکیده [English]

Automatic pavement crack detection is essential for assessing road maintenance and ensuring safe driving. Traditional crack detection has problems such as low efficiency and lack of complete detection. This study aims to solving the problems of traditional crack detection methods and using deep learning models. We proposed a method based on object detection algorithms for pavement crack detection and discussed the latest YOLOv5 series models for pavement crack detection while explaining the theoretical concepts. Finally, a crack detection model and effective pavement management is presented. The proposed model can determine the type, position and geometric characteristics of cracks accurately and at a higher speed in comparison with other methods. For this purpose, the images that had been taken from the asphalt of Mashhad roads were used to train and evaluate the model. Images were labeled for both linear crack and surface crack. Proposed model is developed using five YOLOv5 series algorithms and transfer learning and were evaluated for accuracy and speed of prediction. The models’ accuracy is between 77 to 98% and the prediction speed is between 17.4 to 105 milliseconds, which indicates the optimal performance of the models. The v5s model with 92.8% accuracy and a speed of 23.9 ms is selected as the final model for real prediction of cracks in one of the main thoroughfares of Mashhad. Based on the dimensions and the type of predicted crack and the use of the proposed decision tree, the maintenance approach for each part was determined.

کلیدواژه‌ها [English]

  • Pavement management
  • Crack
  • Machine learning
  • Deep learning
  • YOLOv5

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مدل سازی در مهندسی
دوره 21، شماره 72
فروردین 1402
صفحه 175-193

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  • تاریخ دریافت: 27 خرداد 1401
  • تاریخ بازنگری: 10 مرداد 1401
  • تاریخ پذیرش: 27 شهریور 1401

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