ReviewSensors (Basel, Switzerland)2025
YOLO Object Detection for Real-Time Fabric Defect Inspection in the Textile Industry: A Review of YOLOv1 to YOLOv11.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- An AI-integrated organoid platform enables high-throughput functional evaluation of bioactive metal ions.Bioactive materials · 2026Article
- YOLO12 Down Feather Quality Classification Method Based on A2C2f-CGLU Feature Enhancement.Sensors (Basel, Switzerland) · 2026Article
- Benchmarking Zero-Shot Open-Vocabulary and Fine-Tuned Object Detectors for Underground Mine Personnel Detection.Sensors (Basel, Switzerland) · 2026Article
- YOLOv13-ADR: An Adaptive Deformable Convolution and Neighborhood-Aware Recombination Network for Wind Turbine Blade Defect Detection.Sensors (Basel, Switzerland) · 2026Article
- Active Verification for Missing-Annotation-Aware Tiny Surface Defect Detection in Resistors.Sensors (Basel, Switzerland) · 2026Article
- Article
- Manufacturing-Induced Defect Taxonomy and Visual Detection in UD Tapes with Carbon and Glass Fiber Reinforcements.Polymers · 2026Article
- A Systematic Literature Review of You Only Look Once Architectures (v1-v12) in Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Review
- A Benchmark X-ray Dataset for Pediatric Supracondylar Humerus Fractures with Improved YOLOv11-Based Detection.Scientific data · 2026Article
- A High-Speed Real-Time Sorting Method for Fabric Material and Color Based on Spectral-RGB Feature Fusion.Sensors (Basel, Switzerland) · 2026Article
- Comparative evaluation of YOLOv8, YOLOv11, and RT-DETR for automated microcarrier colonization assessment in bioreactor cell cultures.Frontiers in bioengineering and biotechnology · 2026Article
- A Multi-Parameter Inspection Platform for Transparent Packaging Containers: System Design for Stress, Dimensional, and Defect Detection.Sensors (Basel, Switzerland) · 2025Article
- Dynamic SLAM Dense Point Cloud Map by Fusion of Semantic Information and Bayesian Moving Probability.Sensors (Basel, Switzerland) · 2025Article
- An End-to-End Particle Gradation Detection Method for Earth-Rockfill Dams from Images Using an Enhanced YOLOv8-Seg Model.Sensors (Basel, Switzerland) · 2025Article
- DRL-Driven Intelligent SFC Deployment in MEC Workload for Dynamic IoT Networks.Sensors (Basel, Switzerland) · 2025Article
- Application of Improved YOLOv8 Image Model in Urban Manhole Cover Defect Management and Detection: Case Study.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Automated fabric defect detection is crucial for improving quality control, reducing manual labor, and optimizing efficiency in the textile industry. Traditional inspection methods rely heavily on human oversight, which makes them prone to subjectivity, inefficiency, and inconsistency in high-speed manufacturing environments. This review systematically examines the evolution of the You Only Look Once (YOLO) object detection framework from YOLO-v1 to YOLO-v11, emphasizing architectural advancements such as attention-based feature refinement and Transformer integration and their impact on fabric defect detection. Unlike prior studies focusing on specific YOLO variants, this work comprehensively compares the entire YOLO family, highlighting key innovations and their practical implications. We also discuss the challenges, including dataset limitations, domain generalization, and computational constraints, proposing future solutions such as synthetic data generation, federated learning, and edge AI deployment. By bridging the gap between academic advancements and industrial applications, this review is a practical guide for selecting and optimizing YOLO models for fabric inspection, paving the way for intelligent quality control systems.
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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.