ArticleSensors (Basel, Switzerland)2022
Machine Learning and 3D Reconstruction of Materials Surface for Nondestructive Inspection.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- Machine Learning-Powered Vision for Robotic Inspection in Manufacturing: A Review.Sensors (Basel, Switzerland) · 2026Review
- Deep Learning-Based 3D Reconstruction for Defect Detection in Shipbuilding Sub-Assemblies.Sensors (Basel, Switzerland) · 2026Article
- Deformation Estimation of Textureless Objects from a Single Image.Sensors (Basel, Switzerland) · 2024Article
- Dynamic 3D Measurement without Motion Artifacts Based on Feature Compensation.Sensors (Basel, Switzerland) · 2023Article
- High-Capacity Spatial Structured Light for Robust and Accurate Reconstruction.Sensors (Basel, Switzerland) · 2023Article
- Integrated Video and Acoustic Emission Data Fusion for Intelligent Decision Making in Material Surface Inspection System.Sensors (Basel, Switzerland) · 2022Article
- Intelligent Manufacturing Technology in the Steel Industry of China: A Review.Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
During the steel pipeline installation, special attention is paid to the butt weld control performed by fusion welding. The operation of the currently popular automated X-ray and ultrasonic testing complexes is associated with high resource and monetary costs. In this regard, this work is devoted to the development of alternative and cost-effective means of preliminary quality control of the work performed based on the visual testing method. To achieve this goal, a hardware platform based on a single board Raspberry Pi4 minicomputer and a set of available modules and expansion cards is proposed, and software whose main functionality is implemented based on the systemic application of computer vision algorithms and machine learning methods. The YOLOv5 object detection algorithm and the random forest machine learning model were used as a defect detection and classification system. The mean average precision (mAP) of the trained YOLOv5 algorithm based on extracted weld contours is 86.9%. A copy of YOLOv5 trained on the images of control objects showed a mAP result of 96.8%. Random forest identifying of the defect precursor based on the point clouds of the weld surface achieved a mAP of 87.5%.
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.