Visual anomaly detection is an increasingly important use case for industrial AI, but is not widely used as it requires creating a library of known anomalous samples to train the model to spot deviations in industrial environments. Companies cannot collect real-world samples for every anomaly, especially for unanticipated defects, limiting detection capabilities.
To address this, Edge Impulse’s FOMO-AD architecture can offer a widely accessible platform for visual anomaly detection on any edge device, from GPUs to MCUs. It is the first scalable system capable of training models on an optimal state to detect and catalogue anything outside that baseline as an anomaly in video and image data.
This dramatically increases the productivity of visual inspection systems that will no longer have to be manually trained on anomalous samples before they can start generating real-time insights on-device.
“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 allows customers to build machine learning models by only providing ‘normal’ data.”
Most industrial camera systems capable of computer vision are powered by GPUs and CPUs, with a high install cost that requires wiring and a power-hungry connection to mains electricity. Recent advancements from top-of-the-line silicon manufacturers, and novel edge model architectures, have enable computer vision AI models to operate in either high- or low-power systems, giving businesses more choice.
The benefits of low-power systems include the possibility of building battery-powered visual inspection systems, and lower production costs from using cost-effective hardware that can reduce the overall product form factor.
Edge Impulse has been testing FOMO-AD with customers, in recent months, and has been able to achieve significant results in industrial environments when proactively detecting irregularities in multiple production scenarios. Use of FOMO-AD has led to marked improvements in machine performance and production line efficiencies for customers, according to the company.
Use cases for visual anomaly detection include:
- 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
Edge Impulse’s FOMO-AD architecture is now available and is compatible with all edge devices.
