MO-SOD: Micro-Oxidation Small Object Detection Model for Oxygen-Free Copper Surfaces Based on Microscopic Imaging System.

Micro-oxidation is a fatal problem for some precision oxygen-free copper materials, and it is difficult to detect with the naked eyes. However, manual inspection using microscope equipment is expensive, subjective, and time-consuming. The automatic high-definition micrograph system equipped with micro-oxidation detection algorithm can detect more quickly, efficiently, and accurately. In this study, a micro-oxidation small objection detection model, MO-SOD, is proposed to detect the oxidation degree on oxygen-free copper surface based on microimaging system. This model is developed for rapid detection on the robot platform combined with high-definition microphotography system. The proposed MO-SOD model consists of three modules: small target feature extraction layer, key small object attention pyramid integration layer, and anchor-free decoupling detector. The small object feature extraction layer focuses on the local features of small object to improve the perception of micro-oxidation spots and also takes the global features into account to reduce the impact of noisy background on feature extraction. Key small object attention pyramid integration block couples key small object feature attention and pyramid to detect the micro-oxidation spots in the image. The performance of MO-SOD model is further improved by combining the anchor-free decoupling detector. In addition, the loss function is improved to combine CIOU loss and focal loss to achieve effective micro-oxidation detection. The MO-SOD model is trained and tested from three oxidation levels in an oxygen-free copper surface microscope image data set. The test results show that the average accuracy (mAP) of MO-SOD model is 82.96%, which is superior to other most advanced detectors.

A Feature Fusion Method with Guided Training for Classification Tasks.

In this paper, a feature fusion method with guiding training (FGT-Net) is constructed to fuse image data and numerical data for some specific recognition tasks which cannot be classified accurately only according to images. The proposed structure is divided into the shared weight network part, the feature fused layer part, and the classification layer part. First, the guided training method is proposed to optimize the training process, the representative images and training images are input into the shared weight network to learn the ability that extracts the image features better, and then the image features and numerical features are fused together in the feature fused layer to input into the classification layer for the classification task. Experiments are carried out to verify the effectiveness of the proposed model. Loss is calculated by the output of both the shared weight network and classification layer. The results of experiments show that the proposed FGT-Net achieves the accuracy of 87.8%, which is 15% higher than the CNN model of ShuffleNetv2 (which can process image data only) and 9.8% higher than the DNN method (which processes structured data only).

Distribution of COVID-19 Morbidity Rate in Association with Social and Economic Factors in Wuhan, China: Implications for Urban Development.

Social and economic factors relate to the prevention and control of infectious diseases. The purpose of this paper was to assess the distribution of COVID-19 morbidity rate in association with social and economic factors and discuss the implications for urban development that help to control infectious diseases. This study was a cross-sectional study. In this study, social and economic factors were classified into three dimensions: built environment, economic activities, and public service status. The method applied in this study was the spatial regression analysis. In the 13 districts in Wuhan, the spatial regression analysis was applied. The results showed that: 1) increasing population density, construction land area proportion, value-added of tertiary industry per unit of land area, total retail sales of consumer goods per unit of land area, public green space density, aged population density were associated with an increased COVID-19 morbidity rate due to the positive characteristics of estimated coefficients of these variables. 2) increasing average building scale, GDP per unit of land area, and hospital density were associated with a decreased COVID-19 morbidity rate due to the negative characteristics of estimated coefficients of these variables. It was concluded that it is possible to control infectious diseases, such as COVID-19, by adjusting social and economic factors. We should guide urban development to improve human health.