Texas Instruments
Canvas Category OEM : Semiconductor
We design, manufacture, test and sell analog and embedded semiconductors in markets that include industrial, automotive, personal electronics, communications equipment and enterprise systems.
Assembly Line
Building an Automated Manufacturing Inspection System with FOMO-AD
Just about anything can potentially go wrong in producing a product, so naive approaches that look for specific defects quickly become impractical. For this reason, Bandini decided to put Edge Impulse’s new FOMO-AD algorithm to work. FOMO-AD utilizes a Gaussian mixture model to detect anomalies in conjunction with the powerful and highly efficient FOMO object detection algorithm. This approach allows one to train the model on only normal instances of an object, after which it will be able to recognize any deviations from that normal state. Furthermore, FOMO-AD can pinpoint the locations in an image where anomalies exist, making the inspection process as painless as possible.
Computer vision algorithms tend to be very expensive computationally, but due to the efficiency of the FOMO-AD model, Bandini was able to easily run it on edge computing hardware to keep costs and latency down. In this case, he selected the Texas Instruments SK-TDA4VM development kit. The onboard TDA4VM processor offers eight trillion operations per second of hardware-accelerated AI processing power, which is well more than what is required for the project. Yet the SK-TDA4VM is also inexpensive and requires little power for operation, making it suitable for large-scale deployments. He then paired the kit with a USB webcam to allow it to capture images of components for anomaly detection.
Isolation in Industrial Robot Systems
Smart Factory with TI TDA4VM
Knowing the status and health of factory machinery is critical to an organization’s success. It takes factory operator vigilance to regularly monitor equipment and take action if anomalous behavior is detected. However, it can be fatiguing for personnel to constantly monitor equipment, and if an issue is missed, weeks of downtime for costly repairs can be the result.
This is where the power of computer vision on the edge can be invaluable. Using a computer model trained to detect nominal and off-nominal behavior, operators can be alerted of issues, rather than having to be constantly on the lookout. And with the inferencing being done at the edge, privacy is maintained, and organizational leadership can breathe easy that perhaps sensitive images won’t be sent to the cloud for remote inferencing.