Edge Impulse Enables Visual Anomaly Detection on any Edge Device
July 22, 2024
News
San Jose, California. Edge Impulse announced an innovation enabling visual anomaly detection on any edge device, from NVIDIA GPUs to Arm MCUs, through the first model architecture of its kind, FOMO-AD (Faster Objects, More Objects - Anomaly Detection). To enhance the productivity of visual inspection systems, the scalable FOMO-AD can train models to identify and index anything outside set criteria as an anomaly in video and image data.
“Virtually every industrial customer that wants to deploy computer vision really needs to know when something out of the ordinary happens,” said Jan Jongboom, co-founder and CTO at Edge Impulse. “Traditionally that’s been challenging with machine learning, as classification algorithms need examples of every potential fault state. FOMO-AD uniquely allows customers to build machine learning models by only providing ‘normal’ data.”
According to Edge Impulse, it is currently testing FOMO-AD with businesses and achieving successful results in industrial environments when proactively discovering abnormalities in various use cases.
Ideal Use Cases:
- Industrial:
- Production line inspection, quality control monitoring, defect detection
- Silicon:
- IC inspection, PCB defect detection, soldering inspection
- Automotive:
- Part assembly quality control, crack detection, leak detection, EV battery inspection, painting and surface defect detection
- Medical:
- Medical device inspection, pill inspection, vial contamination inspection, seal inspection
For more information, visit edgeimpulse.com and Edge Impulse’s documentation.
Chad Cox. Production Editor, Embedded Computing Design, has responsibilities that include handling the news cycle, newsletters, social media, and advertising. Chad graduated from the University of Cincinnati with a B.A. in Cultural and Analytical Literature.
