Analysis of AI-Based Single-View 3D Reconstruction Methods for an Industrial Application.

Machine learning (ML) is a key technology in smart manufacturing as it provides insights into complex processes without requiring deep domain expertise. This work deals with deep learning algorithms to determine a 3D reconstruction from a single 2D grayscale image. The potential of 3D reconstruction can be used for quality control because the height values contain relevant information that is not visible in 2D data. Instead of 3D scans, estimated depth maps based on a 2D input image can be used with the advantage of a simple setup and a short recording time. Determining a 3D reconstruction from a single input image is a difficult task for which many algorithms and methods have been proposed in the past decades. In this work, three deep learning methods, namely stacked autoencoder (SAE), generative adversarial networks (GANs) and U-Nets are investigated, evaluated and compared for 3D reconstruction from a 2D grayscale image of laser-welded components. In this work, different variants of GANs are tested, with the conclusion that Wasserstein GANs (WGANs) are the most robust approach among them. To the best of our knowledge, the present paper considers for the first time the U-Net, which achieves outstanding results in semantic segmentation, in the context of 3D reconstruction tasks. Unlike the U-Net, which uses standard convolutions, the stacked dilated U-Net (SDU-Net) applies stacked dilated convolutions. Of all the 3D reconstruction approaches considered in this work, the SDU-Net shows the best performance, not only in terms of evaluation metrics but also in terms of computation time. Due to the comparably small number of trainable parameters and the suitability of the architecture for strong data augmentation, a robust model can be generated with only a few training data.

Ghanaian Women's Knowledge and Perceptions of Services Available to Victims of Intimate Partner Violence.

This article presents qualitative findings on women's knowledge and perceptions of services available to victims of domestic violence in Ghana. In addition, the challenges to access of service and service delivery are explored. Semistructured interviews were conducted with 10 female residents of Sowutuom, a periurban community in Accra, Ghana. An additional three semistructured interviews were also conducted with local service providers in Accra. Results showed that awareness among respondents of available services was low. The majority of women had heard of the Domestic Violence and Victim Support Unit of the Ghana Police Service, though they had limited knowledge of the kind of support provided by this service provider. In addition, most women expressed doubt in the ability of these services to adequately handle cases of intimate partner violence. This study demonstrates that more educational campaigns need to be carried out to raise awareness among Ghanaians on domestic violence and the formal interventions available in the country.