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
翻译:由于车辆日益电气化,没有退路的迹象,汽车应用中部署的电子系统受到比以往更加严格的电磁豁免合规限制,以确保附近电子系统的邻近不会影响其运行。模拟相机链接的EMI合规测试要求监测和评估视频质量,以验证这种合规性,而迄今为止,这种合规性一直是一项手工工作。由于人文解释的性质,这可能会出现不一致。这里,我们建议采用深层学习模型来分析,并使用从EMI合规测试中得出的年级视频内容。这些模型使用完全来自真实测试图像数据的数据集进行培训,以确保生成模型的准确性。从标准亚历克斯网开始,我们建议采用四种模型来对EMI噪音水平进行分类。