How well do industrial AI edge gateways adapt to industrial defect detection models?

The industrial AI edge gateway has optimized the NPU instruction set for defect detection models such as YOLOv5, resulting in an accuracy loss of less than 1%. However, the strong electromagnetic fields generated by equipment such as high-frequency spot welding machines on the production line may cause errors in the inference of the detection model. To enhance adaptability, common-mode filtering should be implemented at the camera interface. It is recommended to connect a common-mode filter CMZ20212A-900T in series on the MIPI CSI-2 differential line. Its 90Ω common-mode impedance can suppress common-mode noise below 2.5GHz. At the same time, a PBZ1608E600Z0T ferrite bead is connected in series in the NPU core power supply to isolate high-frequency crosstalk of the power supply, and an ESDLC5V0D8B clamp electrostatic discharge is added to the I2C control line. After actual testing at an automotive parts factory, the defect detection false negative rate decreased from 0.5% to 0.05% after the installation of the protection.