• Title/Summary/Keyword: Surveying Equipment Performance Testing

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A Study on the Improvement of Performance Testing System of Domestic Surveying Equipment (국내 측량장비 성능검사제도 개선방안 연구)

  • MIN, Kwan-Sik
    • Journal of the Korean Association of Geographic Information Studies
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    • v.19 no.1
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    • pp.53-63
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    • 2016
  • In this paper, we proposed the improvements for performance test and surveying equipment regulations, standards, methods and procedures, depending on the need of improving the legal system for surveying equipment in a diverse and sophisticated surveying industry. This research was performed first investigating the existing legal systems(Act on the establishment and management of spatial data, Framework act on national standards, ISO 17123, JIS B 7912) with respect to the surveying equipment performance testing and the research for IOS and KOLAS suggested the improvements on the application for the surveying equipment performance testing standard. More exactly, first, two years were presented for the surveying equipment performance testing cycle considering the precise accuracy of the instrument stability, purpose and frequency of use, etc. Second, the abolition of the measurement distance by grade and the upward or cross-grade adjustment of the single prism standards about the light wave rangefinder and total station were suggested for the improvement on survey equipment performance criteria. Third, since the main function of total station is focused on a three-dimensional coordinate measurement due to the improvement of surveying equipment performance testing, it was proposed to use the precision(repeatability) of the coordinate measuring method as an evaluation method.

Field Tests for Accuracy of GNSS-RTK Surveys by ISO 17123-8 Standard (ISO 17123-8 표준에 의한 GNSS-RTK 수신기 정확도 평가)

  • Lee, Hungkyu
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.40 no.4
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    • pp.333-342
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    • 2022
  • This paper has theoretically and practically reviewed the ISO (International Standard Organization) 17123-8 standard not only to raise the appropriateness for introducing performance criteria of GNSS (Global Navigation Satellite Systems) surveying equipment based on RTK (Real-Time Kinematic) accuracy but also to derive its proper test procedure by introducing the international standard. Field experiments have been performed to appreciate the GNSS-RTK accuracy of five selected receivers via the full testing procedure of the ISO standard, which statistically compares the so-called experimental standard deviations with themselves and with the reference accuracy. A series of statistical tests have revealed that the RTK accuracy of the same class receivers is not identical, whereas that of the different classes can be equivalent. Such a result evidences the urgency of adopting an RTK accuracy-based specification of the GNSS equipment to the performance standard, currently referenced to the static observation technique only. It is believed that this transition helps the maximization of a new generation of cost-effective receivers to legal surveying applications. Finally, this study proposes the ISO full test, comparing an experimental standard deviation with its referenced value, for a potential field verification procedure of the new performance standard.

Asphalt Concrete Pavement Surface Crack Detection using Convolutional Neural Network (합성곱 신경망을 이용한 아스팔트 콘크리트 도로포장 표면균열 검출)

  • Choi, Yoon-Soo;Kim, Jong-Ho;Cho, Hyun-Chul;Lee, Chang-Joon
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.23 no.6
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    • pp.38-44
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    • 2019
  • A Convolution Neural Network(CNN) model was utilized to detect surface cracks in asphalt concrete pavements. The CNN used for this study consists of five layers with 3×3 convolution filter and 2×2 pooling kernel. Pavement surface crack images collected by automated road surveying equipment was used for the training and testing of the CNN. The performance of the CNN was evaluated using the accuracy, precision, recall, missing rate, and over rate of the surface crack detection. The CNN trained with the largest amount of data shows more than 96.6% of the accuracy, precision, and recall as well as less than 3.4% of the missing rate and the over rate.