Low Cost Two-Stage Fabric Defect Detection at the Edge

[2608.14727] Low Cost Two-Stage Fabric Defect Detection at the Edge Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2608.14727 (cs) [Submitted on 12 Aug 2026] Title: Low Cost Two-Stage Fabric Defect Detection at the Edge Authors: Rasel Hossen , Diptajoy Mistry , Mosaddek Hossain Kamal View a PDF of the paper titled Low Cost Two-Stage Fabric Defect Detection at the Edge, by Rasel Hossen and 2 other authors View PDF HTML (experimental) Abstract: Fabric inspection in the garment industries of low-income economies remains largely manual, and commercial vision systems are priced beyond most small and medium mills. Because defects are sparse under controlled production, a natural response is a cascade: screen every frame with a cheap anomaly detector and invoke a full detector only on suspicious frames. We build such a cascade for four knit-fabric defect classes and deploy it end-to-end on an NVIDIA Jetson Nano with TensorRT FP16. Stage 1 is a compact convolutional autoencoder with decoder attention gates, an edge-weighted reconstruction loss, and feature-level distillation from a frozen YOLOv5n teacher; Stage 2 is YOLOv5n, invoked only on flagged frames. On a 249-image benchmark disjoint from detector training (20 defective, 229 non-defective), Stage 1 at a recall-prioritised threshold flags all 20 defective images (95% CI 0.83-1.00) at a false-positive rate of 49.3% (113/229), reducing false positives by 19.3% relative to a plain autoencoder (p=0.011). The parallel pipeline reaches 13.45 FPS against 9.86 FPS for a sequential YOLO-only loop. Our central finding comes from decomposing that 1.36x: 91% of it is attributable to overlapping JPEG decode with inference rather than to the cascade, which contributes only a 5.1% inference reduction at the measured forwarding rate p = 0.534. We further show that forwarding here is false-positive-limited rather than prevalence-limited - 85% of forwarded frames are false alarms - and quantify the 29-45% inference reduction attainable under tighter calibration. We report this as a caution for cascade speedups measured without controlling the data path, and position the system as AI-assisted triage rather than autonomous acceptance. Comments: 14 pages, 10 figures, 8 tables. Deployment study on NVIDIA Jetson Nano with TensorRT FP16. Includes a decomposition showing the measured 1.36x end-to-end speedup is dominated by data-path overlap rather than by the cascade. Dataset available on Roboflow Universe Subjects: Computer Vision and Pattern Recognition (cs.CV) ACM classes: I.4.9; I.5.4; C.3 Cite as: arXiv:2608.14727 [cs.CV] (or arXiv:2608.14727v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.14727 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Rasel Hossen [ view email ] [v1] Wed, 12 Aug 2026 19:49:00 UTC (9,622 KB) Full-text links: Access Paper: View a PDF of the paper titled Low Cost Two-Stage Fabric Defect Detection at the Edge, by Rasel Hossen and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• AI-Assisted Computational Reproducibility on the FABRIC Testbed#306985
• Completion-Path Credits: Multi-Resource Control for Scale-Up Fabrics#463188
• TIG welding defect detection using ResNet and Random Forest.#418550
• Beyond Diamond: Interpretable Machine Learning Reveals Design Principles for Quantum Defect Host Materials#160895
• Reconstruction-Driven Induction Thermography for AI-Assisted Surface Defect Detection in Welded Structures.#415691