ReviewMaterials (Basel, Switzerland)2020
Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges.
Review in Materials (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
What it found
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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.
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Who cites it
40 citing papers in PubMed.
- Evaluation of different defect-inspection setups for injection molding parts based on the deep learning method.Scientific reports · 2026Article
- Annotated dataset for surface defect detection on ceramic substrates in manufacturing.Data in brief · 2026Article
- Industrial Object Counting from Traditional Machine Vision to Open-World Foundation Models: A Systematic Review.Sensors (Basel, Switzerland) · 2026Review
- A Lightweight and High-Precision PCB Surface Defect Detection Method Based on YOLOv8.Journal of imaging · 2026Article
- SCT-YOLO: A Dual-Stream Defect Detection Network Utilizing Computational Shape, Texture, and Color Features.Sensors (Basel, Switzerland) · 2026Article
- YOLO-DCF: dual distillation and context-aware fusion for defect detection.Scientific reports · 2026Article
- Deep learning based load station inspection for smart manufacturing with limited data.The International journal, advanced manufacturing technology · 2026Article
- Synthetic Data Generation for AI-Based Quality Inspection of Laser Welds in Lithium-Ion Batteries.Sensors (Basel, Switzerland) · 2025Article
- Machine Learning-Driven Prediction of Composite Materials Properties Based on Experimental Testing Data.Polymers · 2025Article
- Deep learning based gasket fault detection: a CNN approach.Scientific reports · 2025Article
- A study on the detection of conductor quantity in cable cores based on YOLO-cable.Scientific reports · 2024Article
- Automatic deep learning detection of overhanging restorations in bitewing radiographs.Dento maxillo facial radiology · 2024Article
- Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey.Biomimetics (Basel, Switzerland) · 2024Review
- Stable 3D Deep Convolutional Autoencoder Method for Ultrasonic Testing of Defects in Polymer Composites.Polymers · 2024Article
- Deep learning-based anomaly detection using one-dimensional convolutional neural networks (1D CNN) in machine centers (MCT) and computer numerical control (CNC) machines.PeerJ. Computer science · 2024Article
- Leveraging Deep Neural Networks for Estimating Vickers Hardness from Nanoindentation Hardness.Materials (Basel, Switzerland) · 2023Article
- Development of microcatheter tube extrusion angle estimation system using convolutional neural network segmentation.Scientific reports · 2023Article
- Quality Control of Carbon Look Components via Surface Defect Classification with Deep Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Research on Internal Shape Anomaly Inspection Technology for Pipeline Girth Welds Based on Alternating Excitation Detection.Sensors (Basel, Switzerland) · 2023Article
- Implementation of a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory Autoencoder for Anomaly Detection on Multivariate Timeseries Data of Industrial Blower Ball Bearing Units.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The detection of product defects is essential in quality control in manufacturing. This study surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects of products, such as electronic components, pipes, welded parts, and textile materials, into categories. Second, recent mainstream techniques and deep-learning methods for defects are reviewed with their characteristics, strengths, and shortcomings described. Third, we summarize and analyze the application of ultrasonic testing, filtering, deep learning, machine vision, and other technologies used for defect detection, by focusing on three aspects, namely method and experimental results. To further understand the difficulties in the field of defect detection, we investigate the functions and characteristics of existing equipment used for defect detection. The core ideas and codes of studies related to high precision, high positioning, rapid detection, small object, complex background, occluded object detection and object association, are summarized. Lastly, we outline the current achievements and limitations of the existing methods, along with the current research challenges, to assist the research community on defect detection in setting a further agenda for future studies.
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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.