The paper presents a multi‑modal deep learning model that uses temporal attention to detect internal welding defects such as porosity, lack of penetration, fusion, undercut, and cold lap in fillet joints during real‑time Gas Metal Arc Welding. Trained on images and sound data from an industrial collaborative welding robot, the model achieves an F1 score of 0.99. Explainable AI techniques are applied to interpret the model’s behavior, highlighting key image and sound spectrogram regions and the most effective modality for each defect type, thereby enhancing trust and reliability in AI‑driven welding inspection.
arXiv:2606. 26260v1 Announce Type: cross Abstract: In laser penetration welding, the assessment of penetration state and weld seam morphology plays a crucial role in determining the weld quality.
This paper presents the IEEE International Conference on Multimedia and Expo (ICME) 2026 Grand Challenge on Cross-Scenario Defect Detection and Fine-Grained Severity Grading for High-Precision Manufacturing. The challenge is motivated by two key limitations of existing industrial defect inspection systems: (1) current deep learning-based methods often suffer significant performance degradation when deployed in unseen production scenarios, and (2) most benchmarks neglect severity-aware assessment, which is critical for risk control and yield optimization.
The paper introduces TEEP‑RCNN, a two‑stage detector that augments Faster R‑CNN with a Feature Pyramid Network backbone and an enhanced Convolutional Block Attention Module (CBAM) featuring dropout in the channel attention MLP and batch‑norm in the spatial attention branch. Training employs a differential learning‑rate schedule with cosine‑annealing warm‑up, and inference uses Test‑Time Augmentation combined with Weighted Box Fusion to stabilize localization of elongated and boundary‑adjacent defects. On the NEU‑DET benchmark, TEEP‑RCNN attains 73.3 % mAP@50 and 37.9 % mAP@50‑95 in only ten epochs on a single GPU, matching or surpassing YOLOv11m while excelling on the rolled‑in‑scale defect category under the COCO metric.
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.
arXiv:2608.20713v1 Announce Type: new Abstract: Generative AI can now produce highly realistic images, yet current models still exhibit subtle but critical defects that undermine their reliability. W...
arXiv:2608.22789v1 Announce Type: new Abstract: Additive Manufacturing (AM) plays a vital role in the ongoing industrial revolution. However, quality control remains crucial and challenging due to pr...
arXiv:2606. 07953v1 Announce Type: new Abstract: Large-scale Visual-Language Models (LVLMs) have achieved remarkable success in natural visual tasks, yet their application to industrial defect detection remains challenging due to two fundamental limitations: (i) the scarcity of large-scale industrial datasets that cover diverse defect categories across multiple domains, and (ii) the reliance on manual prompts (points, boxes, masks) that introduce subjective noise and lack text-visual interaction for fine-grained understanding.
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.
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.
The study evaluates automatic weld seam segmentation using RGB and polarimetric images, comparing convolutional neural networks (CNNs) and transformer architectures. In controlled RGB settings, CNNs achieve a mean mask mAP50 of up to 0.87, but performance drops significantly under uncontrolled conditions. Polarimetric imaging, combined with geometric augmentation, reaches a mean mask mAP50 of up to 093 even in uncontrolled settings, and transformer models, especially RF‑DETR, maintain high accuracy under viewpoint shifts while CNNs fail.
arXiv:2606. 21887v2 Announce Type: replace-cross Abstract: During hot tests on a production line, engine-sound analysis is crucial to ensuring product quality and performance.
Steel surface defect segmentation is critical for industrial quality inspection, yet existing methods struggle with elongated, anisotropic defects such as cracks and scratches due to the isotropic receptive fields of standard convolutions and rigid sampling grids that cannot adapt to irregular defect boundaries. To address these limitations, we propose Strip-based Predictor for Deformable Convolutional Networks (SPDCN) with two key innovations.