Pedestrian detection for complex scenes suffers from pedestrian occlusion issues, such as occlusions between pedestrians. As well-known, compared with the variability of the human body, the shape of a human head and their shoulders changes minimally and has high stability. Therefore, head detection is an important research area in the field of pedestrian detection. The translational invariance of neural network enables us to design a deep convolutional neural network, which means that, even if the appearance and location of the target changes, it can still be recognized effectively. However, the problems of scale invariance and high miss detection rates for small targets still exist. In this paper, a feature extraction network DR-Net based on Darknet-53 is proposed to improve the information transmission rate between convolutional layers and to extract more semantic information. In addition, the MDC (mixed dilated convolution) with different sampling rates of dilated convolution is embedded to improve the detection rate of small targets. We evaluated our method on three publicly available datasets and achieved excellent results. The AP (Average Precision) value on the Brainwash dataset, HollywoodHeads dataset, and SCUT-HEAD dataset reached 92.1%, 84.8%, and 90% respectively.

Sparse representation plays an important role in the research of face recognition. As a deformable sample classification task, face recognition is often used to test the performance of classification algorithms. In face recognition, differences in expression, angle, posture, and lighting conditions have become key factors that affect recognition accuracy. Essentially, there may be significant differences between different image samples of the same face, which makes image classification very difficult. Therefore, how to build a robust virtual image representation becomes a vital issue. To solve the above problems, this paper proposes a novel image classification algorithm. First, to better retain the global features and contour information of the original sample, the algorithm uses an improved non‐linear image representation method to highlight the low‐intensity and high‐intensity pixels of the original training sample, thus generating a virtual sample. Second, by the principle of sparse representation, the linear expression coefficients of the original sample and the virtual sample can be calculated, respectively. After obtaining these two types of coefficients, calculate the distances between the original sample and the test sample and the distance between the virtual sample and the test sample. These two distances are converted into distance scores. Finally, a simple and effective weight fusion scheme is adopted to fuse the classification scores of the original image and the virtual image. The fused score will determine the final classification result. The experimental results show that the proposed method outperforms other typical sparse representation classification methods.

Principal Component Analysis Network (PCANet) is a lightweight deep learning network, which is fast and effective in face recognition. However, the accuracy of faces with occlusion does not meet the optimal requirement for two reasons: 1. PCANet needs to stretch the two-dimensional images into column vectors, which causes the loss of essential image spatial information; 2. When the training samples are few, the recognition accuracy of PCANet is low. To solve the above problems, this paper proposes a multi-scale and multi-layer feature fusion-based PCANet (MMPCANet) for occluded face recognition. Firstly, a channel-wise concatenation of the original image features and the output features of the first layer is conducted, and then the concatenated result is used as the input of the second layer; therefore, more image feature information is used. In addition, to avoid the loss of image spatial information, a spatial pyramid is used as the feature pooling layer of the network. Finally, the feature vector is sent to the random forest classifier for classification. The proposed algorithm is tested on several widely used facial image databases and compared with other similar algorithms. Our experimental results show that the proposed algorithm effectively improves the efficiency of the network training and the recognition accuracy of occluded faces under the same training and testing datasets. The average accuracies are 98.78% on CelebA, 97.58% on AR, and 97.15% on FERET.

Background Porcine epidemic diarrhea (PED) is a contagious intestinal disease caused by porcine epidemic diarrhea virus (PEDV) characterized by vomiting, diarrhea, anorexia, and dehydration, which has caused huge economic losses around the world. However, it is very hard to find completely valid approaches to control the transmission of PEDV. At present, vaccine immunity remains the most effective method. To better control the spread of PED and evaluate the validity of different immunization strategies, 240 PED outbreak cases from 577 swine breeding farms were collected and analyzed. The objective of the present study was to analyze the epidemic regularity of PEDV and evaluate two kinds of different immunization strategies for controlling PED. Results The results showed that the main reasons which led to the outbreak of PED were the movement of pig herds between different pig farms (41.7%) and delaying piglets from the normal production flow (15.8%). The prevalence of PEDV in the hot season (May to October) was obviously higher than that in the cold season (January to April, November to December). Results of different vaccine immunity cases showed that immunization with the highly virulent live vaccine (NH-TA2020 strain) and the commercial inactivated vaccine could significantly decrease the frequency of swine breeding farms (5.9%), the duration of PED epidemic (1.70 weeks), and the week batches of dead piglets (0.48 weeks weaned piglets), compared with immunization with commercial attenuated vaccines and inactivated vaccine of PED. Meanwhile, immunization with the highly virulent live vaccine and the commercial inactivated vaccine could bring us more cash flows of Y̶275,274 per year than immunization with commercial live attenuated vaccine and inactivated vaccine in one 3000 sow pig farm within one year. Conclusion Therefore, immunization with highly virulent live vaccine and inactivated vaccine of PED is more effective and economical in the prevention and control of PED in the large-scale swine farming system.

Sparse representation is an effective data classification algorithm that depends on the known training samples to categorise the test sample. It has been widely used in various image classification tasks. Sparseness in sparse representation means that only a few of instances selected from all training samples can effectively convey the essential class‐specific information of the test sample, which is very important for classification. For deformable images such as human faces, pixels at the same location of different images of the same subject usually have different intensities. Therefore, extracting features and correctly classifying such deformable objects is very hard. Moreover, the lighting, attitude and occlusion cause more difficulty. Considering the problems and challenges listed above, a novel image representation and classification algorithm is proposed. First, the authors’ algorithm generates virtual samples by a non‐linear variation method. This method can effectively extract the low‐frequency information of space‐domain features of the original image, which is very useful for representing deformable objects. The combination of the original and virtual samples is more beneficial to improve the classification performance and robustness of the algorithm. Thereby, the authors’ algorithm calculates the expression coefficients of the original and virtual samples separately using the sparse representation principle and obtains the final score by a designed efficient score fusion scheme. The weighting coefficients in the score fusion scheme are set entirely automatically. Finally, the algorithm classifies the samples based on the final scores. The experimental results show that our method performs better classification than conventional sparse representation algorithms.