مقاله انگلیسی رایگان در مورد شناسایی اتوماتیک کمبود پیاده رو بتنی با یادگیری عمیق – الزویر ۲۰۲۲

مقاله انگلیسی رایگان در مورد شناسایی اتوماتیک کمبود پیاده رو بتنی با یادگیری عمیق – الزویر ۲۰۲۲

 

مشخصات مقاله
ترجمه عنوان مقاله تشخیص خودکار کمبود پیاده رو بتنی و نقشه برداری با یادگیری عمیق
عنوان انگلیسی مقاله Automatic concrete sidewalk deficiency detection and mapping with deep learning
انتشار مقاله سال ۲۰۲۲
تعداد صفحات مقاله انگلیسی ۲۰ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس میباشد
نمایه (index) Scopus – Master Journal List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۹٫۶۰۲ در سال ۲۰۲۰
شاخص H_index ۲۲۵ در سال ۲۰۲۲
شاخص SJR ۲٫۰۷۰ در سال ۲۰۲۰
شناسه ISSN ۰۹۵۷-۴۱۷۴
شاخص Quartile (چارک) Q1 در سال ۲۰۲۰
فرضیه ندارد
مدل مفهومی دارد
پرسشنامه ندارد
متغیر دارد
رفرنس دارد
رشته های مرتبط مهندسی کامپیوتر – مهندسی عمران – مهندسی شهرسازی
گرایش های مرتبط مهندسی نرم افزار – هوش مصنوعی – نقشه برداری – مهندسی ترافیک – طراحی شهری
نوع ارائه مقاله
ژورنال
مجله  سیستم های خبره با برنامه های کاربردی – Expert Systems with Applications
دانشگاه Department of Civil, Marquette University, USA
کلمات کلیدی بینایی کامپیوتر – یادگیری عمیق – تقسیم بندی معنایی – تشخیص اتصالات بتن – کمبود پیاده رو – ابر نقطه
کلمات کلیدی انگلیسی Computer vision – Deep learning – Semantic segmentation – Concrete joint detection – Sidewalk deficiency – Point cloud
شناسه دیجیتال – doi
https://doi.org/10.1016/j.eswa.2022.117980
کد محصول e16766
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
۱٫ Introduction
۲٫ Related works
۳٫ The proposed automated trip hazard detection approach
۴٫ Experimental results and discussions
۵٫ Conclusion
۶٫ Data availability statement
CRediT authorship contribution statement
Declaration of Competing Interest
Acknowledgements
References

بخشی از متن مقاله:

Abstract

     Vertical displacement is a common concrete slab sidewalk deficiency, which may cause trip hazards and reduce wheelchair accessibility. This paper presents an automatic approach for trip hazard detection and mapping based on deep learning. A low-cost mobile LiDAR scanner was used to obtain full-width as-is conditions of sidewalks, after which a method was developed to convert the scanned 3D point clouds into 2D RGB orthoimages and elevation images. Then, a deep learning-based model was developed for pixelwise segmentation of concrete slab joints. Algorithms were developed to extract different types of joints of straight and curved sidewalks from the segmented images. Vertical displacement was evaluated by measuring elevation differences of adjacent concrete slab edges parallel to the boundaries of joints, based on which potential trip hazards were identified. In the end, the detected trip hazards and normal sidewalk joints were geo-visualized with specific information on Web GIS. Experiments demonstrated the proposed approach performed well for segmenting joints from images, with a highest segmentation IoU (Intersection over Union) of 0.88, and achieved similar results compared with manual assessment for detecting and mapping trip hazards but with a higher efficiency. The developed approach is cost- and time-effective, which is expected to enhance sidewalk assessment and improve sidewalk safety for the general public.

Introduction

     Public sidewalks are essential infrastrstructures in cities to provide convenience for urban life. Deficiencies of sidewalks will lead to inconvinence, disruptions and potential hazards to residents. Hence, it is important to monitor and evaluate sidewalk condition such as to take necessary maintenance measures to ensure the normal functionality of sidewalks. To ensure public sidewalks remain in good conditions, local governments usually have their own sidewalk program to assist private property owners (who are the maintaining authority of the sidewalk adjacent to their property) with concrete slab evaluation and defect correction. The typical traditional approach for sidewalk surveying is using smart-level and measuring tools, e.g. tapes, to manually take slope readings and evaluating the compliance with related regulations. However, such manual surveying method takes a long time to assess overall conditions of sidewalks, for example, the City of Middleton and the Village of Shorewood both require eight years to go through each neighborhood (City of Middleton, 2021b, Village of Shorewood, 2021).

Conclusion

     This paper developed and tested a sidewalk trip hazard detection and geo-visualization method that can automatically assess concrete slab deficiencies after obtaining the point clouds via a low-cost LiDAR scanner. Firstly, low-cost mobile LiDAR devices were used to scan sidewalks to obtain the point cloud data, which were then converted to RGB images using the develop tool. Second, a deep learning-based segmentation model U-Net was trained with the sidewalk images to segment concrete joints in the image. Afterwards, joints were extracted from the segmented image and vertical displacements for each joint were evaluated, based on which potential trip hazards were identified and specific information was geo-visualized in Web GIS platform. The experiment results demonstrated the effectiveness of the proposed method. Specifically, the segmentation model performed well for segmenting different types of joints in images (with a highest joint IoU of 0.88) and all the vertical displacement conditions were accurately and comprehensively detected. It was found that integrating the RGB feature with the Normal feature can improve the joint segmentation accuracy of the deep learning model, but the improvement was not significant. For future application, using the point cloud converted orthoimages is sufficient to detect joints. In this study, the segmentation model trained with a few images of straight sidewalks with groover cut contraction (control) joints and the corresponding joint label images already obtained good performance, but adding extra images, such as vegetation covered joints, to enrich the dataset will be considered for future application. Compared to the methods (in Table 2) in existing studies, scanning the as-is condition of the sidewalk with a mobile device is convenient and faster in achieving full-width coverage.

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