Classification of electromagnetic interference induced image noise in an analog video link

TL;DR

This paper introduces deep learning models trained on real test data to automatically classify electromagnetic interference noise in analog video links, improving consistency and accuracy in EMI compliance testing for automotive systems.

Contribution

The paper presents a novel deep learning approach using models based on AlexNet to classify EMI noise levels in video signals, with training on a real-world dataset for enhanced accuracy.

Findings

Deep learning models can effectively classify EMI noise levels.

Training on real test data improves model accuracy.

Proposed models outperform manual assessment methods.

Abstract

With the ever-increasing electrification of the vehicle showing no sign of retreating, electronic systems deployed in automotive applications are subject to more stringent Electromagnetic Immunity compliance constraints than ever before, to ensure the proximity of nearby electronic systems will not affect their operation. The EMI compliance testing of an analog camera link requires video quality to be monitored and assessed to validate such compliance, which up to now, has been a manual task. Due to the nature of human interpretation, this is open to inconsistency. Here, we propose a solution using deep learning models that analyse, and grade video content derived from an EMI compliance test. These models are trained using a dataset built entirely from real test image data to ensure the accuracy of the resultant model(s) is maximised. Starting with the standard AlexNet, we propose four models to classify the EMI noise level