The paper introduces an AI-based visual inspection system for garment sewing-line quality control, employing Convolutional Neural Networks to detect defects such as broken and skipped stitches. The system was trained on black fabric and thread, and tested on various colors, successfully detecting jump defects on black, red, and dark green fabrics but showing limitations on broken defects and fabrics with markedly different visual characteristics. The study highlights that model accuracy depends heavily on training data diversity and the system’s ability to generalize across different fabric and thread colors.
By Ray Wai Man Kong, Ding Ning, Theodore Ho Tin Kong
CF-YOLO introduces a real‑time detection framework for camouflaged micro‑defects on industrial components, combining a Context‑Perception Aggregation Module (CPAM) that fuses large‑kernel macro‑texture cues with small‑kernel boundary details, and a Feature Additive Refinement Module (FARM) that globally refines fine‑grained anomaly representations. The authors also release the Copper Tube Defect Dataset (CTDD), a benchmark of 1,847 images with 4,898 annotated defect boxes. Experiments show CF‑YOLO outperforms baseline detectors such as YOLOv11 by 2.2% in mAP@50 and 3.9% in Precision while preserving real‑time speed.
By Xinda Yu, Kunxin Zheng, Chunan Yu, Qingbo Song, Hao Xiao, Ying Zang, Jie Liu
The paper introduces an automated defect detection system for printed circuit board (PCB) pin insertion, combining U‑Net semantic segmentation with contour‑based feature extraction and logistic regression for board‑level pass/fail classification. Segmentation masks generate contour representations of individual pins, from which features such as average contour size are extracted to train the classifier. Evaluated on an industrial dataset and a public PCB pin‑inspection dataset, the method achieved ROC‑AUC scores of 0.990 and 1.000, outperforming PatchCore and instance‑segmentation baselines.
By Nils Rabeneck, Andr\'e Kiunke, Nicole Hoess, Wolfgang Mauerer
arXiv:2606. 01023v1 Announce Type: cross Abstract: Visual inspection remains the dominant quality-control practice in woven and tufted carpet production, yet it is slow, subjective, and inconsistent at the line speeds and widths of modern looms.
By Akbar Erkinov
The paper introduces YOLOEZ, a no-code, GUI-based tool that streamlines the entire YOLO model workflow—data labeling, training, and inference—for automated structural defect detection. It demonstrates that YOLOEZ outperforms traditional image‑processing methods across most detection metrics while simplifying deployment for users without programming expertise. The tool aims to lower technical barriers in structural health monitoring, enabling broader adoption of AI-driven inspection for predictive maintenance and intelligent structural systems.
By Michael Holm, Tanner McElroy, Xinghang Zhang, Guang Lin
This study evaluates the instance‑segmentation performance of YOLOv11 and YOLOv8 on immature green apples in orchard settings. YOLO11n‑seg achieved the highest mask precision (0.831), while YOLO11m‑seg and YOLO11l‑seg excelled in non‑occluded and occluded fruitlet segmentation. YOLOv8n, however, outperformed the YOLO11 series in inference speed, reaching 3.3 ms compared to 4.8 ms for the fastest YOLO11 model.
By Ranjan Sapkota, Manoj Karkee