Towards Automatic Segmentation and Recognition of Multiple Precast Concrete Elements in Outdoor Laser Scan Data Article Swipe
Jiepeng Liu
,
Dongsheng Li
,
Liang Feng
,
Pengkun Liu
,
Wenbo Wu
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/rs11111383
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/rs11111383
To date, to improve construction quality and efficiency and reduce environmental pollution, the use of precast concrete elements (PCEs) has become popular in civil engineering. As PCEs are manufactured in a batch manner and possess complicated shapes, traditional manual inspection methods cannot meet today’s requirements in terms of production rate of PCEs. The manual inspection of PCEs needs to be conducted one by one after the production, resulting in the excessive storage of finished PCEs in the storage yards. Therefore, many studies have proposed the use of terrestrial laser scanners (TLSs) for the quality inspection of PCEs. However, all these studies focus on the data of a single PCE or a single surface of PCE, which is acquired from a unique or predefined scanning angle. It is thus still inefficient and impractical in reality, where hundred types of PCEs with different properties may exist. Taking this cue, this study proposes to scan multiple PCEs simultaneously to improve the inspection efficiency by using TLSs. In particular, a segmentation and recognition approach is proposed to automatically extract and identify the different types of PCEs in a large amount of outdoor laser scan data. For the data segmentation, 3D data is first converted into 2D images. Image processing is then combined with radially bounded nearest neighbor graph (RBNN) algorithm to speed up the laser scan data segmentation. For the PCE recognition, based on the as-designed models of PCEs in building information modeling (BIM), the proposed method uses a coarse matching and a fine matching to recognize the type of each PCE data. To the best of our knowledge, no research work has been conducted on the automatic recognition of PCEs from a million or even ten million of the outdoor laser scan points, which contain many different types of PCEs. To verify the feasibility of the proposed method, experimental studies have been conducted on the PCE outdoor laser scan data, considering the shape, type, and amount of PCEs. In total, 22 PCEs including 12 different types are involved in this paper. Experiment results confirm the effectiveness and efficiency of the proposed approach for automatic segmentation and recognition of different PCEs.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs11111383
- OA Status
- gold
- Cited By
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- References
- 47
- Related Works
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- OpenAlex ID
- https://openalex.org/W2949302344
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2949302344Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs11111383Digital Object Identifier
- Title
-
Towards Automatic Segmentation and Recognition of Multiple Precast Concrete Elements in Outdoor Laser Scan DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-06-10Full publication date if available
- Authors
-
Jiepeng Liu, Dongsheng Li, Liang Feng, Pengkun Liu, Wenbo WuList of authors in order
- Landing page
-
https://doi.org/10.3390/rs11111383Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/rs11111383Direct OA link when available
- Concepts
-
Computer science, Segmentation, Precast concrete, Matching (statistics), Artificial intelligence, Computer vision, Engineering, Civil engineering, Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
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21Total citation count in OpenAlex
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2025: 4, 2024: 2, 2023: 3, 2022: 4, 2021: 4Per-year citation counts (last 5 years)
- References (count)
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47Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
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| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
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| primary_location.is_published | True |
| primary_location.raw_source_name | Remote Sensing |
| primary_location.landing_page_url | https://doi.org/10.3390/rs11111383 |
| publication_date | 2019-06-10 |
| publication_year | 2019 |
| referenced_works | https://openalex.org/W2110817110, https://openalex.org/W2027085528, https://openalex.org/W2144268533, https://openalex.org/W2042771443, https://openalex.org/W2517726615, https://openalex.org/W2312372683, https://openalex.org/W2792784869, https://openalex.org/W2910920637, https://openalex.org/W6606209374, https://openalex.org/W2033318022, https://openalex.org/W2119512999, https://openalex.org/W2520512835, https://openalex.org/W2767318950, https://openalex.org/W2059122749, https://openalex.org/W2755953131, https://openalex.org/W2152845613, https://openalex.org/W4247410326, https://openalex.org/W1986473362, https://openalex.org/W6734040804, https://openalex.org/W2610087774, https://openalex.org/W2099088762, https://openalex.org/W2790597870, https://openalex.org/W2792850704, https://openalex.org/W1999478155, https://openalex.org/W6649706110, https://openalex.org/W2102402541, https://openalex.org/W1993846506, https://openalex.org/W1989502249, https://openalex.org/W2805917972, https://openalex.org/W6743285607, https://openalex.org/W2173402126, https://openalex.org/W2790606405, https://openalex.org/W2524405979, https://openalex.org/W2767228836, https://openalex.org/W2739432504, https://openalex.org/W2000018820, https://openalex.org/W2157123587, https://openalex.org/W2127218421, https://openalex.org/W1992419399, https://openalex.org/W2145023731, https://openalex.org/W2294798173, https://openalex.org/W2064499898, https://openalex.org/W3041319445, https://openalex.org/W2049981393, https://openalex.org/W2748995840, https://openalex.org/W2591119809, https://openalex.org/W152683297 |
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