دانلود مقاله تشخیص خودکار کمبود پیاده رو و نقشه برداری با یادگیری عمیق
خلاصه
1. مقدمه
2. آثار مرتبط
3. روش تشخیص خودکار خطر سفر پیشنهادی
4. نتایج تجربی و بحث
5. نتیجه گیری
6. بیانیه در دسترس بودن داده ها
بیانیه مشارکت نویسنده CRediT
اعلامیه منافع رقابتی
سپاسگزاریها
منابع
Abstract
1. Introduction
2. Related works
3. The proposed automated trip hazard detection approach
4. Experimental results and discussions
5. Conclusion
6. Data availability statement
CRediT authorship contribution statement
Declaration of Competing Interest
Acknowledgements
References
چکیده
جابجایی عمودی یک کمبود رایج دال بتنی در پیاده رو است که ممکن است باعث خطرات سفر و کاهش دسترسی ویلچر شود. این مقاله یک رویکرد خودکار برای تشخیص خطر سفر و نقشه برداری بر اساس یادگیری عمیق ارائه می دهد. یک اسکنر موبایل ارزان قیمت LiDAR برای به دست آوردن شرایط کاملاً عرض پیاده روها استفاده شد، پس از آن روشی برای تبدیل ابرهای نقطه سه بعدی اسکن شده به تصاویر ارتو و تصاویر ارتفاعی 2 بعدی RGB ایجاد شد. سپس، یک مدل مبتنی بر یادگیری عمیق برای تقسیم بندی پیکسلی اتصالات دال بتنی توسعه یافت. الگوریتم‌هایی برای استخراج انواع مختلف اتصالات پیاده‌روهای مستقیم و منحنی از تصاویر تقسیم‌بندی شده توسعه داده شدند. جابجایی عمودی با اندازه‌گیری اختلاف ارتفاع لبه‌های دال بتنی مجاور به موازات مرزهای اتصالات ارزیابی شد، که بر اساس آن خطرات احتمالی سفر شناسایی شدند. در پایان، خطرات سفر شناسایی شده و درزهای معمولی پیاده رو با اطلاعات خاص در Web GIS تصویربرداری شدند. آزمایش‌ها نشان دادند که رویکرد پیشنهادی برای تقسیم‌بندی مفاصل از تصاویر، با بالاترین بخش‌بندی IoU (تقاطع روی اتحاد) 0.88، به خوبی عمل کرد و نتایج مشابهی را در مقایسه با ارزیابی دستی برای تشخیص و نقشه‌برداری خطرات سفر، اما با کارایی بالاتر، به دست آورد. رویکرد توسعه‌یافته مقرون به صرفه و مقرون به صرفه است، که انتظار می‌رود ارزیابی پیاده‌رو را افزایش دهد و ایمنی پیاده‌رو را برای عموم مردم بهبود بخشد.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
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.