Latest Lattice sensAI™ solution stack delivers industry-leading power efficiency, expanded AI model support, and flexible deployment tools for next-generation edge applications ‒ ...

Abstract: To address the issues of low accuracy, high missed detection rates, and insufficient robustness in strip steel surface defect detection under complex industrial environments, this paper ...

1 School of Internet of Things Engineering, Jiangnan University, Wuxi, China 2 School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, China Wood, a widely ...

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 ...

1 Drone Lab, Centre for Artificial Intelligence and Robotics, Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India 2 Drone Lab, School of Mechanical and Materials Engineering, Indian ...

New issue New issue Open Open [Ray Data]Pylint detection found some Python code defects in ray data #53881 ...

Scientists from the federally funded Argonne National Laboratory in Illinois and the University of Virginia have developed a new approach for detecting defects in metal parts produced by 3D printing.

This repository contains the code and resources for a PCB defect detection project. The project uses YOLO and other comparative models to detect and classify PCB defects, along with improvements to ...

Detecting sub-5nm defects creates huge challenges for chipmakers, challenges that have a direct impact on yield, reliability, and profitability. In addition to being smaller and harder to detect, ...