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
arXiv:2607. 21577v1 Announce Type: cross Abstract: Quality control in printing, particularly in rotogravure printing, still depends on slow, costly, and subjective manual inspection.
By Korota Ars\`ene Coulibaly, Mohamed Hamlich, Khalid Hmali, Andrea Trombin
WireSeg-32K is a new synthetic dataset designed for wire instance segmentation, comprising 32,000 RGB images, instance masks, depth maps, and a complementary real-world test set with annotations. The dataset is generated using DeformX, a co-simulation pipeline that couples Cosserat-rod dynamics with photorealistic Isaac Sim rendering to produce physically plausible, contact-consistent wire shapes and diverse scenes. Fine-tuning SAM3 with LoRA on WireSeg-32K improves real-world mAP@75 by 10.2% compared to the off-the-shelf model, demonstrating the transferability of physically grounded synthetic data.
By Zilin Dai, Lehong Wang, Yi Yang, Xiang Fei
arXiv:2604. 26633v2 Announce Type: replace-cross Abstract: Industrial surface defect inspection suffers from a fundamental data bottleneck: defects are rare, annotations require expert knowledge, and collecting balanced training sets is slow and costly.
By Paul Julius K\"uhn, Mika Pommeranz, Arjan Kuijper, Saptarshi Neil Sinha
arXiv:2608. 11053v1 Announce Type: cross Abstract: The application of computer vision in agriculture has shown significant potential for improving crop monitoring and precision farming.
By Ismail Ismail Tijjani, Sunusi Muhammad Ibrahim, Amina Ibrahim Khaleel, Lanre Olusegun Akinola, Fatima Isa Jibrin, Muhammad Bashir Aliyu, Abdullahi Abdussalam Dalhat, Abdullahi Suiudeen
The paper presents a deep‑learning perception framework for selective robotic cotton harvesting, evaluated on 1,008 field images captured under diverse lighting and weather conditions. Detection models from YOLOv8 to YOLOv13 were benchmarked, with GELAN‑s achieving the best trade‑off between accuracy and speed. For segmentation, YOLOv12‑m‑seg outperformed other models, and a detection‑prompted segmentation approach using GELAN‑s bounding boxes further improved localization for SAM variants. Field trials with a UR5e robot and ZED2i camera confirmed YOLOv12‑m‑seg’s real‑time performance for cotton boll detection, segmentation, and selective picking.
By Thevathayarajh Thayananthan, Xin Zhang, Isuru Laddusinghe Badu, Jonathan Harjono, Glen C. Rains, Beiwen Li, Leonardo M. Bastos, Nuwan K. Wijewardane, Vitor S. Martins
arXiv:2606. 06536v1 Announce Type: cross Abstract: Automated defect detection in high-voltage transmission-line insulators remains challenging due to severe class imbalance, large scale variation, and the small spatial extent of defect instances in Unmanned Aerial Vehicle (UAV) imagery.
By Malak Allam, Khaled Shaban, Ali Hamdi