Qu Zanxi, Li Li, Jin Weiqi, Yang Ye
J Opt Soc Am A Opt Image Sci Vis. 2024 Mar 1;41(3):500-509. doi: 10.1364/JOSAA.499820.
Binocular vision technology is widely used to acquire three-dimensional information of images because of its low cost. In recent years, the use of deep learning for stereo matching has shown promising results in improving the measurement stability of binocular vision systems, but the real-time performance in high-precision networks is typically poor. Therefore, this study constructed a deep-learning-based stereo matching binocular vision system based on the BGLGA-Net, which combines the advantages of past networks. Experiments showed that the ability to detect the edges of foreground objects was enhanced. The network was used to build a system on the Xavier NX. The measurement accuracy and stability were better than those of traditional algorithms.
双目视觉技术因其成本低而被广泛用于获取图像的三维信息。近年来，将深度学习用于立体匹配在提高双目视觉系统的测量稳定性方面已显示出有前景的结果，但高精度网络的实时性能通常较差。因此，本研究构建了一种基于BGLGA-Net的基于深度学习的立体匹配双目视觉系统，该系统结合了以往网络的优点。实验表明，检测前景物体边缘的能力得到了增强。该网络被用于在Xavier NX上构建一个系统。测量精度和稳定性优于传统算法。
