تشخیص و طبقه بندی خودکار ترک سنگفرش
ترجمه نشده

تشخیص و طبقه بندی خودکار ترک سنگفرش

عنوان فارسی مقاله: تشخیص و طبقه بندی خودکار ترک سنگفرش با استفاده از شبکه توجه به ویژگی چند مقیاسی
عنوان انگلیسی مقاله: Automatic Pavement Crack Detection and Classification Using Multiscale Feature Attention Network
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی فناوری اطلاعات، زمین شناسی
گرایش های تحصیلی مرتبط: شبکه های کامپیوتری، زمین شناسی مهندسی
کلمات کلیدی فارسی: تشخیص ترک سنگفرش، طبقه بندی ترک، شبکه عصبی پیچشی، استخراج ویژگی چند مقیاسی، مکانیسم توجه
کلمات کلیدی انگلیسی: Pavement crack detection, crack classification, convolutional neural network, multiscale feature extraction, attention mechanism
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2956191
دانشگاه: School of Geomatics, Liaoning Technical University, Fuxin 123000, China
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 12
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14056
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


I. Introduction


II. Materials and Methods


III. Experiment and Analysis


IV. Discussion


V. Conclusion


Authors


Figures


References

نمونه متن انگلیسی مقاله

Abstract


Pavement crack detection and characterization is a fundamental part of road intelligent maintenance systems. Due to the high non-uniformity of cracks, topological complexity, and similar noise from crack texture, the challenge arises in this domain with automated crack detection and classification in a complex environment. In this work, an overarching framework for a universal and robust automatic method that simultaneously characterizes the type of crack and its severity level was developed. For crack detection, we propose a novel and efficient crack detection network that captures the crack context information by establishing a multiscale dilated convolution module. On this foundation, an attention mechanism is introduced to further refine the high-level features. Moreover, the rich features at different levels are fused in an upsampling module to generate more detailed crack detection results. For crack classification, a novel characterization algorithm is developed to classify the type of crack after detection. The crack segment branches are then merged and classified into four types: transversal, longitudinal, block, and alligator; the severity levels of cracks are assessed by calculating the average width and distance between the crack branches. The proposed crack detection method effectively detects crack information in a complex environment, and achieves the current state-of-the-art accuracy. Compared to manual classification results, the classification accuracy of transversal and longitudinal cracks is higher than 95%, and the classification accuracy of block and alligator is above 86%.


Introduction


Automatic detection and classification of pavement cracks is an important part of intelligent transportation systems and acts as a primary rapid analysis of pavement distresses. The implementation of a fast and accurate automatic pavement crack detection system is essential for maintaining and monitoring complex transportation networks, and is an effective way to improve the road service quality [1]. Pavement crack automatic detection and characterization systems perform three primary tasks: data acquisition, crack detection, and crack classification. With the development of mobile mapping technology and hardware storage devices, fast acquisition devices are becoming more widely used in pavement distress screening [2] as they can quickly obtain road distress data. Fig. 1(a) shows a road surface image acquisition device installed on a roof, whereas Fig. 1(b) is a pavement image taken vertically, which can be used to measure the crack location and for qualitative analysis. In recent years, a numerous experts and scholars have devoted themselves to researching automatic detection of pavement cracks, and have obtained promising research results [3], [4]. At present, the research on automatic detection of pavement cracks is roughly divided into three methods: traditional image processing methods, machine learning methods, and deep learning methods.

  • اشتراک گذاری در

دیدگاه خود را بنویسید:

تاکنون دیدگاهی برای این نوشته ارسال نشده است

تشخیص و طبقه بندی خودکار ترک سنگفرش
نوشته های مرتبط
مقالات جدید
لوگوی رسانه های برخط

logo-samandehi

پیوندها