Computational prediction of crystal materials properties can help to do large-scale insiliconscreening. Recent studies of material informatics have focused on expert design of multidimensionalinterpretable material descriptors/features. However, successes of deep learning suchas Convolutional Neural Networks (CNN) in image recognition and speech recognition havedemonstrated their automated feature extraction capability to effectively capture the characteristicsof the data and achieve superior prediction performance. Here, we propose CNN-OFM-Magpie, aCNN model with OFM (Orbital-field Matrix) and Magpie descriptors to predict the formationenergy of 4030 crystal material by exploiting the complementarity of two-dimensional OFM featuresand Magpie features. Experiments showed that our method achieves better performance thanconventional regression algorithms such as support vector machines and Random Forest. It is alsobetter than CNN models using only the OFM features, the Magpie features, or the basic one-hotencodings. This demonstrates the advantages of CNN and feature fusion for materials propertyprediction. Finally, we visualized the two-dimensional OFM descriptors and analyzed the featuresextracted by the CNN to obtain greater understanding of the CNN-OFM model.
Deep learning-based bridge crack detection methods have advantages over traditional methods. We proposed an automated bridge crack detection method using lightweight vision models. First, our study applied the You Only Look Once 4th version (YOLO v4) (Bochkovskiy et al. in Yolov4: Optimal speed and accuracy of object detection. arXiv:200410934, 2020) to bridge surface crack detection. Then, to achieve model acceleration, some lightweight networks were used to replace the feature extraction network in YOLO v4, which reduced the parameter numbers and the backbone layers. The lightweight design can reduce the computational overhead of the model, making it convenient to deploy on edge platforms with limited computational power. The experimental results showed that the lightweight network-based bridge crack detection model required significantly less storage space at the expense of a slight reduction in precision. Therefore, an improved YOLO v4 crack detection method was proposed to meet real-time running without sacrificing accuracy. The precision, recall, and F1 score of the proposed crack detection method are 93.96%, 90.12%, and 92%, respectively. And the model only required 23.4 MB of storage space, and its frames per second could reach 140.2 frames. Compared with existing bridge crack detection methods, the proposed method showed precision, speed, and model size advantages.
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