Anomaly Detection for any industrial edge device

  • April 24, 2024
  • William Payne

Edge AI specialist Edge Impulse has launched a new technology for visual anomaly detection on edge devices. FOMO-AD (Faster Objects, More Objects – Anomaly Detection) is a model architecture that encompasses edge platforms from NVIDIA GPUs to Arm MCUs,.

Visual anomaly detection is an important use case for industrial AI. It is not currently widely used as it requires creating a library of known anomalous samples to train the model to spot deviations in industrial environments.

Edge Impulse’s FOMO-AD architecture offers a platform for visual anomaly detection on any edge device, from GPUs to MCUs. It is capable of training models on an optimal state to detect and catalogue anything outside that baseline as an anomaly in video and image data.

Edge Impulse says this 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 uniquely 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 from companies like Edge Impulse, 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.

In recent months, Edge Impulse has been testing FOMO-AD with customers, achieving proven 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.

Manufacturing 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