Department of Computer and Control Engineering, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, Al. Powstańców Warszawy 12, 35-959 Rzeszow, Poland.
Sensors (Basel). 2023 Jul 4;23(13):6134. doi: 10.3390/s23136134.
The article presents an algorithm for the multi-domain visual recognition of an indoor place. It is based on a convolutional neural network and style randomization. The authors proposed a scene classification mechanism and improved the performance of the models based on synthetic and real data from various domains. In the proposed dataset, a domain change was defined as a camera model change. A dataset of images collected from several rooms was used to show different scenarios, human actions, equipment changes, and lighting conditions. The proposed method was tested in a scene classification problem where multi-domain data were used. The basis was a transfer learning approach with an extension style applied to various combinations of source and target data. The focus was on improving the unknown domain score and multi-domain support. The results of the experiments were analyzed in the context of data collected on a humanoid robot. The article shows that the average score was the highest for the use of multi-domain data and data style enhancement. The method of obtaining average results for the proposed method reached the level of 92.08%. The result obtained by another research team was corrected.
本文提出了一种基于卷积神经网络和风格随机化的室内场所多领域视觉识别算法。作者提出了一种场景分类机制，并基于来自不同领域的合成和真实数据改进了模型的性能。在所提出的数据集，域变化被定义为相机模型的变化。使用从多个房间收集的图像数据集来显示不同的场景、人类活动、设备变化和照明条件。所提出的方法在多领域数据的场景分类问题中进行了测试。基础是一种转移学习方法，扩展风格应用于源和目标数据的各种组合。重点是提高未知域分数和多域支持。实验结果在人形机器人上收集的数据背景下进行了分析。文章表明，在使用多领域数据和数据风格增强方面，平均得分最高。所提出方法的平均结果获取方法达到了 92.08%的水平。另一个研究团队的结果被纠正了。
