Rong Wang

Chinese Academy of Sciences

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Qifeng Li

Tianjin University

Ronghua Gao

National Engineering Research Center for Information Technology in Agriculture

Luyu Ding

National Engineering Research Center for Information Technology in Agriculture

Bir Bhanu

University of California, Riverside

Jianwei Ding

China People's Public Security University

Chunjiang Zhao

National Engineering Research Center for Information Technology in Agriculture

Weihong Ma

National Engineering Research Center for Information Technology in Agriculture

Qianli Zhou

Sichuan Normal University

Chunjiang Zhao

National Engineering Research Center for Information Technology in Agriculture

Ligen Yu

Harbin Medical University

Cooperative Institutions

Chinese Academy of Sciences

National Engineering Research Center for Information Technology in Agriculture

China People's Public Security University

University of Chinese Academy of Sciences

National Animal Husbandry Service

Institute of Subtropical Agriculture

Northwest A&F University

University of Electronic Science and Technology of China

Ministry of Agriculture and Rural Affairs

Beijing Institute of Technology

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Deep learning SPECT lung perfusion image classification method based on attention mechanism

Journal of Physics Conference Series (2021)

Sitao Zeng Yongchun Cao Qiang Lin Zhengxing Man Tao Deng

Abstract SPECT lung perfusion is an important functional imaging technology. It can capture the functional lesions of the lung in a non-invasive manner and has become an important clinical detection method for diseases such as pulmonary embolism. In order to realize the automatic detection of the degree of pulmonary embolism, this paper studies and constructs a deep classification model based on the attention mechanism. First, the normalization technique is used to convert the original lung perfusion file into a SPECT image; secondly, in view of the over-fitting phenomenon of the deep learning model caused by the small amount of medical image data and the unbalanced data, the image translation and rotation techniques are used to perform effective expansion; then, in order to improve the model’s feature extraction ability, the attention mechanism is combined with the depth classification model to build a SPECT lung perfusion image classification model; finally, a set of real SPECT lung perfusion images were used to carry out comparative experiments on various depth classification models. The experimental results show that the model proposed in this paper can effectively detect the extent of lung disease lesions, and the classification accuracy rate exceeds 88%, which verifies the effectiveness and reliability of the classification model.

Normalization

10.1088/1742-6596/1748/4/042050

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Citations (4)

A Soybean Image Noise Smoothing Method Using Multi-scale Morphology

Yuhua Chai Danting Zhao Rong Wang

A soybean image noise smoothing method using multi-scale morphology is presented in this paper. The method is based on manipulating the intensity of scale-specific features present in the noisy image. Features at various scales are extracted by means of morphological filtering. The proposed scheme has been implemented by using morphological towers to smooth noise in images. The algorithm has been tested on sample images corrupted with various kinds of noise. The results have been compared with those of other standard methods. It is found that the performance of the proposed method is satisfactory and superior to other methods referred in this paper.

Smoothing

Image noise

Sample (material)

10.1109/his.2009.302

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Citations (3)

Pig Face Recognition Based on Metric Learning by Combining a Residual Network and Attention Mechanism

Agriculture (2023)

Rong Wang Ronghua Gao Qifeng Li Jiabin Dong

As machine vision technology has advanced, pig face recognition has gained wide attention as an individual pig identification method. This study establishes an improved ResNAM network as a backbone network for pig face image feature extraction by combining an NAM (normalization-based attention module) attention mechanism and a ResNet model to probe non-contact open-set pig face recognition. Then, an open-set pig face recognition framework is designed by integrating three loss functions and two metrics to finish the task with no crossover of individuals in the training and test sets. The SphereFace loss function with the cosine distance as a metric and ResNAM are combined in the framework to obtain the optimal open-set pig face recognition model. To train our model, 37 pigs with a total of 12,993 images were randomly selected from the collected pig face images, and 9 pigs with a total of 3431 images were set as a test set. 900 pairs of positive sample pairs and 900 pairs of negative pairs were obtained from the images in the test set. A series of experimental results show that our accuracy reached 95.28%, which was 2.61% higher than that of a human face recognition model. NAM was more effective in improving the performance of the pig face recognition model than the mainstream BAM (bottleneck attention module) and CBAM (convolutional block attention module). The research results can provide technological support for non-contact open-set individual recognition for intelligent farming processes.

Normalization

Three-dimensional face recognition

10.3390/agriculture13010144

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Citations (16)

Verification Learning for Robust Visual Object Tracking

Communications in computer and information science (2019)

Linyu Fei Rong Wang Jianwei Ding

BitTorrent tracker

Benchmark (surveying)

Tracking (education)

10.1007/978-981-13-9917-6_46

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Computer Vision-Based Measurement Techniques for Livestock Body Dimension and Weight: A Review

Agriculture (2024)

Weihong Ma Xiangyu Qi Yi Sun Ronghua Gao Luyu Ding

Acquiring phenotypic data from livestock constitutes a crucial yet cumbersome phase in the breeding process. Traditionally, obtaining livestock phenotypic data primarily involves manual, on-body measurement methods. This approach not only requires extensive labor but also induces stress on animals, which leads to potential economic losses. Presently, the integration of next-generation Artificial Intelligence (AI), visual processing, intelligent sensing, multimodal fusion processing, and robotic technology is increasingly prevalent in livestock farming. The advantages of these technologies lie in their rapidity and efficiency, coupled with their capability to acquire livestock data in a non-contact manner. Based on this, we provide a comprehensive summary and analysis of the primary advanced technologies employed in the non-contact acquisition of livestock phenotypic data. This review focuses on visual and AI-related techniques, including 3D reconstruction technology, body dimension acquisition techniques, and live animal weight estimation. We introduce the development of livestock 3D reconstruction technology and compare the methods of obtaining 3D point cloud data of livestock through RGB cameras, laser scanning, and 3D cameras. Subsequently, we explore body size calculation methods and compare the advantages and disadvantages of RGB image calculation methods and 3D point cloud body size calculation methods. Furthermore, we also compare and analyze weight estimation methods of linear regression and neural networks. Finally, we discuss the challenges and future trends of non-contact livestock phenotypic data acquisition. Through emerging technologies like next-generation AI and computer vision, the acquisition, analysis, and management of livestock phenotypic data are poised for rapid advancement.

10.3390/agriculture14020306

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Citations (7)

A Self-directed Reading Environment for Learning Pronunciation of Japanese Kanji based on Collaborative-building Database

E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (2006)

Fei Yuan Jing Yuan Rong Wang Hiroyuki Mitsuhara Kazuhide Kanenishi

Pronunciation

Source

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Referring image segmentation with attention guided cross modal fusion for semantic oriented languages

Frontiers of Computer Science (2022)

Qianli Zhou Rong Wang Hai‐Miao Hu Quange Tan Wenjin Zhang

10.1007/s11704-022-1136-3

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Citations (2)

Research on the Measurement of Antennas Radiation Characteristics Based on Small Unmanned Aerial Vehicle Platform

Procedia Computer Science (2018)

Juyong Zhang Qiaoling Shuang Zhiping Chen Rong Wang Jingxin Liu

In order to solve the problem that the radiation characteristics of non-rotating antenna in their operative conditions is difficult to be measured, we present a measurement method based on small unmanned aerial vehicle (UAV) platform. Firstly, a set of measurement system is established. Then, we provide a solution of the key issues about how to acquire the position data of small UAV and design signal synchronization. Secondly, we analyze position precision of small UAV and introduce a factor of polarization mismatch to compensate errors. Finally, based on this measurement system, the scheme design of linear polarization antenna's far-field measurement is developed. This platform has been built and the related experiments will be carried out later. The measurement method proposed in this paper is in the working environment without changing their position of the antenna, which is very important for large and medium-sized antenna and array antenna measurements.

Position (finance)

10.1016/j.procs.2018.04.229

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Citations (3)

Distribution Characteristics and Factors Influencing Culturable Bacterial Bioaerosols on a Dairy Farm in Northern China

Agriculture (2023)

Lin Ru Luyu Ding Deng Shuhui Qifeng Li Wanying Zhao

Studying the distribution characteristics of bioaerosols and their interaction with the environment is crucial for dairy farms. The distribution of aerosols differs in dairy farming from farming of other livestock, and their sensitivity to environmental factors varies across sites. Field experiments were conducted in an intensive commercial dairy farm in Northern China to investigate the horizontal and vertical distribution of culturable bacterial bioaerosols. Concentration levels and particle size ranges were analyzed, and the impact of multiple environmental factors on culturable bacterial bioaerosols was assessed. Significant variations in culturable bacterial bioaerosol concentrations were observed across eight functional zones, ranging from 1.14 × 103 to 7.35 × 103 CFU/m3. Culturable bacterial bioaerosols exhibited consistent carrier distribution patterns across six different size ranges. Vertical analysis revealed significantly higher culturable bacterial bioaerosol concentrations at a 1 m height compared to 4 m (p < 0.05), while similar size distributions were observed at different heights of the same sampling location. The top three environmental factors influencing culturable bacterial bioaerosol concentrations were PM100 concentration, wind direction, and air temperature. This study provides insights into the distribution characteristics of culturable bacterial bioaerosols on dairy farms and their response to environmental factors. The findings serve as a reference for evaluating bioaerosol emissions and establishing daily disinfection management measures on dairy farms.

Indoor bioaerosol

Bioaerosol

10.3390/agriculture13091752

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Citations (5)

Face Recognition Based on Densely Connected Convolutional Networks

Tong Zhang Rong Wang Jianwei Ding Xin Li Bo Li

The face recognition methods based on convolutional neural network have achieved great success. The existing model usually used the residual network as the core architecture. The residual network is good at reusing features, but it is difficult to explore new features. And the densely connected network can be used to explore new features. We proposed a face recognition model named Dense Face to explore the performance of densely connected network in face recognition. The model is based on densely connected convolutional neural network and composed of Dense Block layers, transition layers and classification layer. The model was trained with the joint supervision of center loss and softmax loss through feature normalization and enabled the convolutional neural network to learn more discriminative features. The Dense Face model was trained using the public available CASIA-WebFace dataset and was tested on the LFW and the CAS-PEAL-Rl datasets. Experimental results showed that the densely connected convolutional neural network has achieved higher face verification accuracy and has better robustness than other model such as VGG Face and ResNet model.

Softmax function

Discriminative model

Robustness

Normalization

10.1109/bigmm.2018.8499078

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Citations (15)