Automated solar filament detection using deep learning faces several challenges. Semantic segmentation of solar filaments is a complicated multiscale feature extraction task with long-tail distribution.
The study introduces a leakage‑safe evaluation protocol for detecting hydrogen embrittlement in 316L stainless steel using SEM micrographs. By employing a Leave‑One‑Region‑Out cross‑validation over 14 spatial regions, the authors compare six feature‑classifier combinations, finding that a simple local binary pattern (LBP) with a support vector machine (SVM) achieves the best performance (balanced accuracy 0.79, H2 recall 0.69, H2 precision 0.82). The results are statistically significant (p = 0.008) and suggest that texture descriptors can reliably recover hydrogen‑charging signatures even with limited data.
By Muhammad Awais, Muhammad Yaseen, Abdul Shakoor, Niaz Ahmed Niaz, Huria Zia, Muhammad Zain Shakoor
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
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
arXiv:2606. 00852v1 Announce Type: cross Abstract: Printed circuit board (PCB) defect detection is challenging because many defects are small and difficult to distinguish from complex background patterns.
By Vinay Edula, Nilesh Badwe, Priyanka Bagade
arXiv:2606. 23851v1 Announce Type: new Abstract: This work investigates the implementation of artificial intelligence and machine learning (AI/ML) for real-time monitoring in laser powder bed fusion (LPBF) additive manufacturing.
By Inioluwa Emmanuel, Zhuo Yang, Ho Yeung, Xinyao Zhang