ArticleSensors (Basel, Switzerland)2022
Automated Dimension Estimation of Steel Pipes Stacked at Construction Sites Using Euclidean Distances Calculated by Overlapping Segmentation.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Pipes are construction materials for water and sewage, air conditioning, firefighting, and gas facilities at construction sites. The quantification and identification of pipes stacked at construction sites are indispensable and, thus, are directly related to efficient process management. In this study, an automated CNN-based technique for estimating the diameter and thickness of the pipe in an image is proposed. The proposed method infers the thickness of the pipe through the difference by segmentation, by overlapping the inside and outside circles for a single pipe. When multiple pipes are included in the image, the inside and outside circles for the identical pipe are matched through the spatial Euclidean distance. The CNN models are trained using pipe images of various sizes to segment the pipe circles. An error of less than 7.8% for the outer diameter and 15% for the thickness is verified through execution with a series of 50 testing pipe images.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.