The subject of the work described in this article is the detection of false synchronization in the transmission of digital stereo images. Until now, the synchronization problem was solved by using start triggers in the recording. Our proposal checks the discrepancy between the received pairs of images, which allows you to detect delays in transferring images between the left camera and the right camera. For this purpose, a deep network is used to classify the analyzed pairs of images into five classes: MuchFaster, Faster, Regular, Slower, and MuchSlower. As can be seen as a result of the conducted work, satisfactory research results were obtained as the correct classification. A high percentage of average probability in individual classes also indicates a high degree of certainty as to the correctness of the results. An author’s base of colorful stereo images in the number of 3070 pairs is used for the research.

中文翻译：

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利用卷积神经网络检测立体图像传输的虚假同步

本文介绍的工作主题是检测数字立体声图像传输中的错误同步。到目前为止，通过在记录中使用启动触发器解决了同步问题。我们的建议检查了接收到的图像对之间的差异，这使您能够检测到左右相机之间传输图像的延迟。为此，使用深度网络将已分析的图像对分为五类：MuchFaster，Faster，Regular，Slower和MuchSlower。从所进行的工作可以看出，获得了令人满意的研究结果作为正确的分类。各个类别中平均概率的高百分比也表明了结果正确性的高度确定性。