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13 pages, 1641 KiB

Open AccessArticle

Computational Integral Imaging Reconstruction Based on Generative Adversarial Network Super-Resolution

by Wei Wu, Shigang Wang, Wanzhong Chen, Zexin Qi, Yan Zhao, Cheng Zhong and Yuxin Chen

Appl. Sci. 2024, 14(2), 656; https://doi.org/10.3390/app14020656 - 12 Jan 2024

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Abstract

To improve acquisition efficiency and achieve super high-resolution reconstruction, a computational integral imaging reconstruction (CIIR) method based on the generative adversarial network (GAN) network is proposed. Firstly, a sparse camera array is used to generate an elemental image array of the 3D object. Then, the elemental image array is mapped to a low-resolution sparse view image. Finally, a lite GAN super-resolution network is presented to up-sample the low-resolution 3D images to high-resolution 3D images with realistic image quality. By removing batch normalization (BN) layers, reducing basic blocks, and adding intra-block operations, better image details and faster generation of super high-resolution images can be achieved. Experimental results demonstrate that the proposed method can effectively enhance the image quality, with the structural similarity (SSIM) reaching over 0.90, and can also reduce the training time by about 20%. Full article

(This article belongs to the Special Issue Pattern Recognition Based on Machine Learning and Deep Learning)

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13 pages, 3878 KiB

Open AccessArticle

City Architectural Color Recognition Based on Deep Learning and Pattern Recognition

by Yi Zhuang and Chenyi Guo

Appl. Sci. 2023, 13(20), 11575; https://doi.org/10.3390/app132011575 - 23 Oct 2023

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Abstract

The collection of information about buildings and their colors is an important aspect of urban planning. The intelligent recognition of buildings using image information plays a significant role in the development of smart cities and urban planning. This thesis proposes a building color-recognition technique based on morphological features utilizing convolutional neural networks and the K-means clustering algorithm of image-recognition technology. The proposed method can identify buildings in images and classify them into two categories, buildings and panoramas, for color extraction and matching. This method involves training convolutional neural networks on deep learning so that the buildings can be differentiated and segmented. Subsequently, the K-means algorithm extracts colors from the segmented building images. The extracted building category, color, and text information were analyzed to obtain a comparison and analysis results of buildings and panoramas. The results demonstrated that the system is capable of accurately segmenting buildings, as well as extracting colors from both buildings and panoramas separately. It can also contribute to the extraction and presentation of color schemes in smart city planning and provide valuable insights for the future development of urban colors. Full article

(This article belongs to the Special Issue Pattern Recognition Based on Machine Learning and Deep Learning)

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22 pages, 17083 KiB

Open AccessArticle

Real-Time Optical Detection of Artificial Coating Defects in PBF-LB/P Using a Low-Cost Camera Solution and Convolutional Neural Networks

by Victor Klamert, Timmo Achsel, Efecan Toker, Mugdim Bublin and Andreas Otto

Appl. Sci. 2023, 13(20), 11273; https://doi.org/10.3390/app132011273 - 13 Oct 2023

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Abstract

Additive manufacturing plays a decisive role in the field of industrial manufacturing in a wide range of application areas today. However, process monitoring, and especially the real-time detection of defects, is still an area where there is a lot of potential for improvement. High defect rates should be avoided in order to save costs and shorten product development times. Most of the time, effective process controls fail because of the given process parameters, such as high process temperatures in a laser-based powder bed fusion, or simply because of the very cost-intensive measuring equipment. This paper proposes a novel approach for the real-time and high-efficiency detection of coating defects on the powder bed surface during the powder bed fusion of polyamide (PBF-LB/P/PA12) by using a low-cost RGB camera system and image recognition via convolutional neural networks (CNN). The use of a CNN enables the automated detection and segmentation of objects by learning the spatial hierarchies of features from low to high-level patterns. Artificial coating defects were successfully induced in a reproducible and sustainable way via an experimental mechanical setup mounted on the coating blade, allowing the in-process simulation of particle drag, part shifting, and powder contamination. The intensity of the defect could be continuously varied using stepper motors. A low-cost camera was used to record several build processes with different part geometries. Installing the camera inside the machine allows the entire powder bed to be captured without distortion at the best possible angle for evaluation using CNN. After several training and tuning iterations of the custom CNN architecture, the accuracy, precision, and recall consistently reached >99%. Even defects that resembled the geometry of components were correctly classified. Subsequent gradient-weighted class activation mapping (Grad-CAM) analysis confirmed the classification results. Full article

(This article belongs to the Special Issue Pattern Recognition Based on Machine Learning and Deep Learning)

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