论文标题
模拟视频链接中电磁干扰引起的图像噪声的分类
Classification of electromagnetic interference induced image noise in an analog video link
论文作者
论文摘要
由于没有撤退迹象的车辆的不断增加的电气化，部署在汽车应用中的电子系统会受到比以往任何时候都更严格的电磁免疫依从性约束，以确保附近电子系统的接近性不会影响其运行。模拟摄像机链接的EMI合规性测试需要监视和评估视频质量以验证此类合规性，到目前为止，这是一项手动任务。由于人类解释的性质，这是不一致的。在这里，我们提出了一种使用深度学习模型的解决方案，并从EMI合规检验中得出的等级视频内容。这些模型是使用完全由实际测试图像数据构建的数据集训练的，以确保最大化所得模型的准确性。从标准Alexnet开始，我们提出了四个模型来对EMI噪声水平进行分类
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
