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With the ever-increasing electrification of the vehicle showing no sign ofretreating, electronic systems deployed in automotive applications are subjectto more stringent Electromagnetic Immunity compliance constraints than everbefore, to ensure the proximity of nearby electronic systems will not affecttheir operation. The EMI compliance testing of an analog camera link requiresvideo quality to be monitored and assessed to validate such compliance, whichup 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 learningmodels that analyse, and grade video content derived from an EMI compliancetest. These models are trained using a dataset built entirely from real testimage data to ensure the accuracy of the resultant model(s) is maximised.Starting with the standard AlexNet, we propose four models to classify the EMInoise level

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