Abstract: Automated detection of weld defects in steel pipe cladding remains challenging due to strong reflections from metallic surfaces and low-contrast defect characteristics. To address these ...
Researchers from Stony Brook University, in collaboration with Ecosuite and Ecogy Energy, have developed a self-supervised machine-learning algorithm designed to identify physical anomalies in solar ...
数据集构建过程中，我们采用了多种数据增强策略，包括旋转、缩放、亮度调整、对比度增强等，以增加模型的泛化能力 ...
A new technical paper titled “DECOR: Deep Embedding Clustering with Orientation Robustness” was published by researchers at Oregon State University and Micron Technology. “In semiconductor ...
The dataset is already organized in YOLO format in the steel_dataset/ directory. If you need to reorganize from original format, see utility/reorganize_dataset.py. steel-defect-detection/ ├── ...
ABSTRACT: Regular pipeline inspections are crucial for timely identification of critical defects and ensuring pipeline integrity. To address the challenges of detecting defects in PE gas pipelines ...
To address the issues of missed detection and false detection during the defect inspection process of the PCB, an improved YOLOv7-based algorithm for PCB defect detection is proposed. Firstly, the ...
TDK SensEI’s edgeRX Vision system, powered by advanced AI, accurately detects defects in components as small as 1.0×0.5 mm in real time. Operating at speeds up to 2000 parts per minute, it reduces ...
Defect detection requirements on the order of 10 defective parts per million (DPPM) are driving improvements in inspection tools’ resolution and throughput at foundries and OSATs. However, defects ...
Abstract: Aiming at the problems of low automation and long detection cycle of urban drainage pipeline defect detection tasksan improved YOLOX-s object detection algorithm was proposed. Based on YOLOX ...
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