Abstract: Image defect detection has become an essential process in manufacturing, quality control, and safety assurance, ensuring that defective products are identified before reaching consumers.

Built a real-time, purely classical computer vision system for fabric defect detection using multi-method analysis (GLCM, FFT, Gabor, statistical variance, background subtraction, and edge–Hough), ...

NVIDIA leverages generative AI and vision foundation models to enhance semiconductor defect classification, addressing limitations of traditional CNNs and improving manufacturing efficiency. As the ...

DDA provides real-time defect detection capabilities for manufacturing quality control using computer vision and machine learning. The system runs at the edge using AWS IoT Greengrass, enabling ...

Abstract: The development of effective defect detection methods for underwater pipelines plays an important role in energy transportation safety. Although high-frequency guided waves provide ...

Introduction: Accurate defect detection in dissimilar metal welds (DMWs) remains a major challenge due to heterogeneous microstructures and imaging noise. Methods: In this study, we propose a novel ...

External defect detection is a crucial step in Orah mandarin citrus grading. However, in existing defect detection algorithms by image processing, Orah mandarin surfaces exhibit characteristics such ...

YOLOv7 is an advanced deep learning algorithm for object detection, representing an improved version of the YOLO (You Only Look Once) series. Utilizing an end-to-end training approach based on deep ...

When it comes to non-destructive testing (NDT), choosing the right defect recognition software for manufacturing is critical. When it comes to non-destructive testing (NDT), choosing the right defect ...