Handling large amounts of data has become a key for developing automated driving systems. Especially for developing highly automated driving functions, working with images has become increasingly challenging due to the sheer size of the required data. Such data has to satisfy different requirements to be usable in machine learning-based approaches. Thus, engineers need to fully understand their large image data sets for the development and test of machine learning algorithms. However, current approaches lack automatability, are not generic and are limited in their expressiveness. Hence, this paper aims to analyze a state-of-the-art text and image embedding neural network and guides through the application in the automotive domain. This approach enables the search for similar images and the search based on a human understandable text-based description. Our experiments show the automatability and generalizability of our proposed method for handling large data sets in the automotive domain.
翻译：处理大量数据已成为发展自动驾驶系统的关键。特别是针对高度自动化的驾驶功能开发，由于所需数据的规模庞大，处理图像越来越具有挑战性。这些数据必须满足不同的要求才能在基于机器学习的方法中使用。因此，工程师需要完全了解其大型图像数据集，以便开发和测试机器学习算法。但是，现有方法缺乏自动化、不具有通用性，并且在表达方面受到限制。因此，本文旨在分析一种最先进的文本和图像嵌入神经网络，并指导在汽车领域中的应用。这种方法使得可以根据类似图像和人类可理解的基于文本描述进行搜索。我们的实验表明，该方法可自动化和通用化，适用于处理汽车领域的大型数据集。
相关内容
微信扫码咨询专知VIP会员