کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
555011 1451261 2016 18 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data
ترجمه فارسی عنوان
عبارات کیفی و بصری و مدل های عمیق سلسله مراتبی برای تشخیص و تشخیص علامت های ترافیکی در داده های اسکن لیزر موبایل
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر سیستم های اطلاعاتی
چکیده انگلیسی

This paper presents a novel algorithm for detection and recognition of traffic signs in mobile laser scanning (MLS) data for intelligent transportation-related applications. The traffic sign detection task is accomplished based on 3-D point clouds by using bag-of-visual-phrases representations; whereas the recognition task is achieved based on 2-D images by using a Gaussian-Bernoulli deep Boltzmann machine-based hierarchical classifier. To exploit high-order feature encodings of feature regions, a deep Boltzmann machine-based feature encoder is constructed. For detecting traffic signs in 3-D point clouds, the proposed algorithm achieves an average recall, precision, quality, and F-score of 0.956, 0.946, 0.907, and 0.951, respectively, on the four selected MLS datasets. For on-image traffic sign recognition, a recognition accuracy of 97.54% is achieved by using the proposed hierarchical classifier. Comparative studies with the existing traffic sign detection and recognition methods demonstrate that our algorithm obtains promising, reliable, and high performance in both detecting traffic signs in 3-D point clouds and recognizing traffic signs on 2-D images.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: ISPRS Journal of Photogrammetry and Remote Sensing - Volume 113, March 2016, Pages 106–123
نویسندگان
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