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The aim of this article is to propose a defect identification method for bare printed circuit boards (PCB) based on multi-feature fusion. This article establishes a description method for various features of grayscale, texture, and deep semantics of bare PCB images. First, the multi-scale directional projection feature, the multi-scale grey scale cooccurrence matrix feature, and the multi-scale gradient directional information entropy feature of PCB were extracted to build the shallow features of defect images. Then, based on migration learning, the feature extraction network of the pre-trained Visual Geometry Group16 (VGG-16) convolutional neural network model was used to extract the deep semantic feature of the bare PCB images. A multi-feature fusion method based on principal component analysis and Bayesian theory was established. The shallow image feature was then fused with the deep semantic feature, which improved the ability of feature vectors to characterize defects. Finally, the feature vectors were input as feature sequences to support vector machines for training, which completed the classification and recognition of bare PCB defects. Experimental results show that the algorithm integrating deep features and multi-scale shallow features had a high recognition rate for bare PCB defects, with an accuracy rate of over 99%. [ABSTRACT FROM AUTHOR]

Generative Adversarial Networks (GANs) emerged thanks to the development of deep neural networks. Forgery images generated by various variants of GANs are widely spread on the Internet, which may be damage personal credibility and cause huge property losses. Thus, numerous methods are proposed to detect forgery images, but most of them are designed to detect forgery faces. Therefore, a method to detect forgery images of various scenes is proposed. In this work, central difference convolution and vanilla convolution (CDC‐Mix) are mixed after considering the depth and width features of neural networks and analyzing the influence of attention on network performance. Based on CDC‐Mix, a separable convolution (SeparableCDC‐Mix) is proposed. The proposed method consists of three parts: (1) CDC‐Mix and SeparableCDC‐Mix are used to extract the gradient information and texture features; (2) CDCM is used to extract the multi‐scale information of the image; (3) multi‐scale fusion module (MS‐Fusion) is used to fuse the multi‐scale information from different locations of the network. A large number of experiments have been carried out on several datasets generated by GAN, and the experimental results show that the proposed method has a great improvement compared with the existing advanced methods. [ABSTRACT FROM AUTHOR]

Construction materials undergo appearance and textural changes during the construction process. Accurate recognition of these changes is critical for effectively understanding the construction status; however, recognizing the various levels of detailed material conditions is not sufficiently explored. The primary challenge in the detailed recognition of the conditions of the material is the availability of labeled training data. To address this challenge, this study proposes a novel state-of-the-art deep learning model that leverages transfer learning, utilizing the pretrained Inception V3 to transfer knowledge to the limited labeled data set in the construction context. This enables the model to learn meaningful representations from the limited training data, enhancing its ability to accurately classify material conditions. In addition, gray-level co-occurrence matrix (GLCM)–based texture features are extracted from the images to capture the appearance and textural changes in construction materials, which are then concatenated with the transferred convolutional neural network (CNN) features to create a more comprehensive representation of the material conditions. The proposed model achieved an overall classification accuracy of 95% and 71% with limited (208 images) and very small (70 images) data sets, respectively. It outperformed different experimental architectures, including CNN models developed using limited data with and without augmentation, CNN model with data augmentation and transfer learning, separate models using local binary pattern (LBP) and GLCM texture features with super learners trained using augmented limited data. The findings suggest that the proposed model, which combines transfer learning with GLCM-based texture features, is effective in accurately recognizing the conditions of construction materials, even with limited labeled training data. This can contribute to improved construction management and monitoring. There are several practical applications of the proposed model combining CNN architecture with GLCM textures in construction industry. The primary applications are in activities such as quality inspection and progress monitoring. By analyzing images of different material conditions, such as concrete, plaster, or masonry walls, the model can be used to automatically detect defects or inconsistencies. This enables construction practitioners to ensure the quality of their structures, detect issues early on, and make informed decisions for maintenance and repair. Additionally, the model can be used to monitor the progress of construction projects by analyzing images to track the status and completion of different building components to estimate delays or cost overruns comparing with the expected material condition at a given time of the schedule. Moreover, because the model does not depend on large training data set, it enables construction managers to develop their project specific data sets and automate material condition detection and monitoring. [ABSTRACT FROM AUTHOR]