bluuebunny/crossref_metadata_2025 huggingface dataset:
Crowd density estimation method based on floor area
Authors: Feng Fujian , Liu Shuang , Pan Yongzheng , He Xin , Wei Jiayin , Wang Lin
Published: November 2020

Crowd density estimation is a hot issue in the analysis of crowd abnormal behavior. At present, the crowd density is calculated by the ratio of population and area. However, these methods ignore the influence of monitoring perspective and local crowd distribution in the selection and processing of area. In this paper, we propose a crowd density estimation method based on the floor area, which can accurately estimate the density of low and medium density crowd. Based on crowd density estimation, we propose a new division method of crowd density rank, which can effectively judge the crowd density rank at each moment. The proposed method is executed by the data sets of Mall, Smarty City and USCD. The experimental results show that proposed method can effectively estimate the crowd density value in the low and medium density crowd.
Effect of natural background on detection threshold of radioactive material detection system
Authors: Li Yan , Zhao Hongtao , Li Gang , Zhao Xiaowen , Wu Peng
Published: November 2020

In this paper, the relationship between detection limit and error probability is studied based on the statistical law of radioactivity. The conversion method between the lowest detectable radioactivity count and the activity can be obtained, which provides the basis for setting the alarm threshold reasonably.
Multi-sensor scheduling method based on Cuckoo and Particle Swarm optimization algorithm
Authors: Wei Wenfeng , Liu Changyun , Wang Chuang , Li Junxia
Published: November 2020

In order to solve the problem that particle swarm optimization (PSO) tends to fall into the local optimal solution and the convergence speed is slow when solving multi-sensor resource scheduling model, a cuckoo particle swarm optimization (CPSO) algorithm is proposed on the basis of PSO. On the basis of target tracking model, the multi-sensor scheduling model is established. Then the LEVY flight was introduced from cuckoo algorithm into particle swarm algorithm, the algorithm can jump out of the local optimal solution as soon as possible and improve the convergence speed and accuracy of the algorithm. The simulation results show that the improved algorithm is effectively improved in terms of convergence speed and accuracy, and is applied to the solution of sensor scheduling model to further enhance the optimization ability and achieve good results.
Research on Landscape Design Based on Digital Technology
Authors: Jun Yan , Xian Shan Xiao
Published: November 2020

Firstly, this paper briefly analyzes the concept and development of digital technology. Then it deeply analyzes the application research of digital technology, including digital projection, VR technology and mixed reality. Finally, it expounds the application research of digital technology in landscape design, and puts forward the design strategy of improving the projection technology and VR virtual reality technology in digital technology from the current situation of landscape design development in China, so as to promote the integration of digital technology in landscape design development.
A surface defect identification method based on improved threshold segmentation algorithm
Authors: Feng Xinglong , Gao Xianwen , Luo Ling
Published: November 2020

For enterprises, defect detection is very important because it is related to the quality of products produced by enterprises. With the development of machine vision, accurate analysis of image data benefits defect detection. In an enterprise that produces electronic cigarettes, professional and technical personnel used to detect a defect in a workpiece by using manual testing. The defect detection rate of this method is only 95%, and the efficiency is low. We use an improved threshold segmentation method to solve this problem in this paper and we have achieved success. Compared with typical methods, the accuracy of our proposed algorithm reaches over 99% and at the same time, the detection efficiency has been improved by more than 50%. Our method also has the advantage of simplicity, practicality and low cost.
Research and application of improved K-means based on MapReduce
Authors: Wang Hongqin , Wang Hongxia , Jiang Li , Pan Zhengjun
Published: November 2020

With the development of big data, the traditional data mining clustering algorithm K-Means is inefficient and has poor scalability in dealing with massive data. MapReduce on the Hadoop platform was used to realize the parallel processing of the K-Means algorithm, the performance of the algorithm was tested by experiments. The results show that the improved K-Means algorithm has good parallel expansion capability, high efficiency, and great potential when processing big data mining. The algorithm is applied to the big data processing of customer consumption in a restaurant chain, and the effectiveness of the algorithm is verified, which can better serve the decision of restaurant.
Real Terrain Simulation based on Point Cloud of Water Area and Land Area
Authors: Ma Haibo
Published: November 2020

The joint simulation of land and water integration plays an increasingly important role in surveying and mapping, water conservancy, and land resources supervision. Based on the theory of 3D coordinate transformation, this paper discusses the application of Rodrigues’s matrix and indirect adjustment model in the iterative calculation of point cloud registration parameters. In this method, anti-symmetric matrix elements are used to form a rotation matrix, which avoids the complex trigonometric function operation of traditional methods, and improves the efficiency of calculation while ensuring the accuracy, and can easily calculate the initial value of parameters. Experimental results demonstrate that the proposed method has the advantages of high accuracy, robustness, stability, and reliability.
Exploration of the digital display design method of ancient coconut shell hill archaeological site
Authors: Cai Pei , Zhang Xiaopu , Yu Yue
Published: November 2020

With the development of science and computer technology, digital display design breaks through geographical and time and space constraints, not only can realize the restoration and protection of ancient coconut shell mound ruins, at the same time, the rapid spread of ancient coconut shell mound site culture was realized. This paper analyze the current situation of the ancient coconut shell mound site display and discover the core content of the ancient coconut shell mound site display to explore a solution to the digital display design of the ancient coconut shell mound site. The digital display design is conducive to the cultural information dissemination of the ancient coconut shell mound site, and improves the interactive experience and satisfaction of visitors; it aims to play a certain guiding role for the ancient coconut shell mound site to truly and completely explain the value of the site and spread the connotation of the site.
Monitoring of rice lodging using Sentinel-1 data
Authors: Wang Jinning , Li Kun , Shao Yun , Wang Zhiyong
Published: November 2020

Synthetic Aperture Radar (SAR) can monitor rice regardless of time and weather condition, and the ability of crop lodge monitoring has been demonstrated by previous studies. However, there have no study about monitoring of rice lodging using satellite-based SAR. In this study, we extracted backscatter coefficient (BC) and H/A/Alpha polarimetric parameters from dual-polarized Sentinel-1 SAR data, and a lodging sensitivity factor γ was constructed for selecting optimal radar feature parameter (ORFP) which is highly sensitivity to lodging rice. Then, the decision tree classification method was used with multiple ORFPs to classify the healthy and lodging rice plots. We firstly acquired shape of rice region using Sentinel-2 image and Maximum likelihood classification method for eliminating the influence of other features before classification of lodging rice. The result showed that the overall accuracy of 87.18% is achieved with the combination of ORFPs and decision tree.
Correlation inversion detection algorithm and imaging simulation of wood defects focused by stress signal based on Symlets wavelet
Authors: Zeng Bin , Li Xuan , Mao Zhenwei
Published: November 2020

Aiming at the problems of traditional wood natural defect detection methods such as difficult to remove interference signals, weak signal enhancement, and low defect detection accuracy, a wood natural defect detection method based on Symlets wavelet and inversion reconstructed signals is proposed. First, the defect information is extracted by multi-scale slice based on Sym3 wavelet. Then, the high-frequency abnormal signal components of the defect stress after reconstruction are “focused” correlated inversion detection and analysis, and the defect information extraction process based on the inversion “focusing” is realized. Finally, the characteristic signals of natural defects in wood are effectively separated to achieve high-precision detection and three-dimensional image reconstruction of the position and shape of the defects. This method has achieved good experimental simulation results, and the recognition accuracy of wood knots and other defects is over 97%.
The Study of Locating Diseased Leaves Based on RPN in Complex Environment
Authors: Guo Yan , Zhang Jin , Su Pei , Hou Guang Hua , Deng Fang Yi
Published: November 2020

Plant disease is one of the major factors threatening the plant growth. In this paper, we utilize the region proposed network (RPN) to detect and locate the plant leaf based on the machine deep learning algorithm. Firstly, the original image needs to be input into convolution neural network (CNN). After several convolution and pooling operations, highly condensed image features can be obtained. Secondly, a reference boundary frame for predicting the position of an object can be obtained by sliding nine boundary frames as sliding windows on the feature map. Two neural networks are input into each boundary box to get the classification result and boundary location. Finally, with the help of non-maximum suppression algorithm (NMS), multiple boundary boxes for the same object are eliminated and only the best boundary boxes are retained. Experiments show that RPN algorithm has better performance on locating the diseased leaves in complex environment, thus reducing the influence of disease on agricultural production. At the same time, it is of great significance in economic development, ecological protection, agricultural production and other fields.
Investigation of Blind Multi-User Detection Technology
Authors: Tuo Xingyu , Zeng Wei , Zhu Zhiyuan , Guo Jing
Published: November 2020

Blind multi-user detection algorithm is an effective method to overcome multi-access interference and influence of near-far effect. This paper briefly describes commonly used algorithms of blind multi-user detection technology, introduces the application of the least mean square algorithm (LMS) and compares the performance with other algorithms. Aiming at the shortcomings of the LMS algorithm, the variable step size LMS algorithm and the relatively low complexity LMK algorithm based on the change of the input signal vector are studied.
Research on the University Fall Enrollment Strategies Based on Apriori Algorithm
Authors: Gao Xia , Liu Shucheng , Xu Fangqin
Published: November 2020

Using the historical enrollment data of colleges and universities to mine, discover the relationship between the potential data, and use the results of data mining to provide reasonable suggestions for enrollment policies, which is of great significance to college enrollment. This paper aims at the allocation of enrollment colleges and the allocation of college students in the process of autumn enrollment in colleges and universities, through the Apriori algorithm in data mining, mining historical enrollment data for 3 consecutive years after data cleaning. Based on the frequent itemsets obtained from the experimental results, a reasonable allocation of college places and student college allocation plan was made.
Study of An Environmentally Friendly Oil Pipeline Cleaning Technology Based on Self-Propelled Intelligent Robot
Authors: He Nana , Li Taofeng
Published: November 2020

Polymers, coke, oil dirt, limescale, sediments and rust corrosives, which are adhered to pipe wall, will be generated during the oil pipeline transport, thus leading to degradation of production efficiency, increasing the energy consumption and seriously impacting oil transport quality, efficiency and safety. What’s worse, this will result in blockage and thus interrupt the flow and suspend the production. Based on modern artificial intelligence (AI) and new-type environmental protection technologies, a self-propelled robot of intelligent manipulation is proposed and enters oil pipeline to conduct environmentally friendly cleaning using high-pressure water jet, and the cleaning wastewater can be recycled. This environmentally friendly intelligent pipeline robot is applicable to detection and high-pressure water jet cleaning of circular variable-sectional-diameter pipelines in closed environment. Furthermore, it is capable of online pipe wall quality detection for horizontal, vertical and turning circular pipelines, and also cleaning effect evaluation. According to the operating principle, an environmentally friendly oil pipeline cleaning technology based on intelligent robot is proposed by combining the technical characteristics in the existing oil pipeline design and construction, in an effort to provide a reference for intelligent cleaning and automatic visual inspection and evaluation of oil pipelines.
Design of Filtering Power Divider with Wide Stopband Based on QMSIW
Authors: Cheng Yi , Huang Zhixiang , Wang Zhongmiao , Wang Chao , Li Xuemei
Published: November 2020

A miniaturized filtering power divider with wide stopband based on quarter-mode substrate integrated waveguide (QMSIW) is proposed. The physical size of the QMSIW is reduced by 3/4 comparing with the traditional SIW structure, which can achieve miniaturization, and its electromagnetic field distribution has little effect. Using the metal interference column and the etched U-shaped groove to adjust the resonance frequency, so that the main mode and the first higher mode form a passband, meanwhile, suppressing the second higher mode. The simulation results show that the center frequency is 4.42 GHz with insertion loss of 0.45 dB, the 3-dB bandwidth is 4.12-4.63 GHz with a fractional bandwidths of 9.7%, and there is a great stopband up to 11 GHz with 15 dB rejection level. Besides, the return loss of passband is better than 17.5 dB. To verify the validity of this design, a prototype is fabricated and measured. The measured results demonstrate that it has a good agreement with the design simulations.
A Low SAR, Small size, High gain, Circularly polarized Antenna for Wearable Application
Authors: Wang Chao , Zhang Liang , Cheng Yi , Li XueMei , Wu Xianliang
Published: November 2020

A planar, small size, high gain, low specific absorption rate (SAR) and circularly-polarized (CP) wearable antenna is proposed in this paper. The antenna is fabricated on a substrate with a dielectric constant of 3.5 with a thickness of 0.5 mm and sized at 42×42 mm only. Both the simulation results show that the overlap bandwidth of impedance and axial ratio of the proposed antenna is 13.7% (5.15-5.91 GHz). To isolate the antenna against the influence of the human body, a artificial magnetic conductor (AMC) plane is added on the reverse side of the radiator. The AMC structure effectively isolates the human body from the radiation of the antenna and reduces the specific absorption rate (SAR) of it by more than 96.1% at 5.5 GHz, which improves the antenna gain and the peak gain reaches 5.09 dBi at 5.5 GHz. The wearable performance of the antenna was analyzed, and it is observed that it can sustain good performance even under human body loading. Beside, the antenna has a small size, which makes it ideal for wearable applications.
Spacecraft in Orbit Fault Prediction Based on Deep Machine Learning
Authors: Zhang Xiangyan , Wang Xiaochen , Tian Huadong
Published: November 2020

Spacecraft is playing a more and more important role in military reconnaissance, environmental disaster reduction and other fields in china. The research on fault prediction technology based deep machine learning will realize the transformation from traditional post fault processing to prior system maintenance. In this paper, combined with different in orbit fault modes of spacecraft and the complexity of the fault modes, multi kind of prediction methods for single parameter and multi parameter faults are proposed. Based on this, the in orbit fault prediction system is constructed, and for the results of a typical in orbit case show that the prediction results are good. In the future, it can be considered to further expand the fault prediction methods of the system, such as modeling the in orbit data directly by the graph neural network algorithm, so as to further improve the prediction accuracy.
A Design of Hign Gain Wideband Circular Polarization Antenna Based on All-Metal Vivaldi Antenna
Authors: Li Xuemei , Huang Zhixiang , Liu Dui , Wang Chao , Cheng Yi
Published: November 2020

This paper designs a broadband circularly polarized antenna covering the 25 GHz to 31 GHz band. The Vivaldi antenna is used as the basic structure to generate broadband electromagnetic waves; circular polarization is achieved by feeding through a circularly polarized bridge; a metal choke ring is used at the bottom to ensure the low side lobes of the antenna. The antenna consists of four basic Vivaldi antennas, a metal choke ring, a grounded substrate, and a feed network. The results show that the antenna covers the entire frequency band when the axial ratio is lower than 3 dB, and the impedance matching is better. The use of a choke ring structure ensures that the antenna electromagnetic waves are radiated upward, resulting in higher gain and lower side lobes. The highest gain in the included frequency band is 11.4 dB, and the simulation result is good. The antenna has high gain and low sidelobe characteristics.
An Optimized LightGBM Model for Fraud Detection
Authors: Huang K
Published: November 2020

The rapid development of e-commerce and the growing popularity of credit cards have made online transactions smooth and convenient. However, large numbers of online transactions are also the targets of online credit card fraud, which aggregate to enormous losses annually. In response to this trend, many machine learning and deep learning methods have been proposed to solve this problem. Unfortunately, most models have been developed on small datasets and require tedious fine-tuning processes. In this paper, a LightGBM-based method for fraud detection is proposed. The dataset used for this study is the IEEE-CIS Fraud Detection dataset provided by Vesta Corporation, which includes over 1 million samples. Experiments have shown that the LightGBM-based method outperforms most classical methods based on Support Vector Machine, XGBoost, or Random Forest. Besides, effective feature engineering methods for feature selection and Bayesian fine-tuning for automatic hyperparameter searching are also proposed.
Structural damage detection system based on FPGA and EMI method
Authors: Huang Dehang , Li Tianli , Tao Yongxiang
Published: November 2020

This paper designs a miniaturized structure health detection system based on the electro-mechanical impedance (EMI) method. The system uses two orthogonal signals of the same frequency as the excitation source, which effectively solves the problem of determining the phase difference in phase detection. According to the characteristics of the piezoelectric sensor, the FPGA is used as the main control chip, the measurement frequency range is adjustable by the user, and transmitted through the wireless serial port. The admittance curve of the piezoelectric sensor is displayed on the host computer, and real-time calculation of the RMSD value and relative RMSD value of the admittance curve. Compared with the current impedance analyzer on the market, it has the characteristics of wide measurement frequency range, small size and low cost.
Automatic machine learning Framework for Forest fire forecasting
Authors: Qu Jintao , Cui Xiaohui
Published: November 2020

Based on the automatic machine learning framework, combined with the characteristics of forest fire meteorological data and the adaptive requirements of forest fire prediction, this paper optimizes the data preprocessing, parameter learning, loss function and other links of auto-sklearn, builds a forest fire risk prediction framework with regional adaptive characteristics. Based on the forest meteorological fire risk data, a forest fire risk prediction model with regional characteristics and self-learning characteristics is constructed to solve the problems of low compatibility of the existing machine learning methods with binary unbalanced forest fire data, improve the accuracy of forest fire prediction and provide decision-making basis for forestry risk management. The comparative analysis results show that the prediction accuracy of the improved framework in different test sets is improved by 13% on average. Compared with the existing machine learning model for forest fire prediction, the prediction accuracy of the framework proposed in this paper is comprehensively better than the existing methods in terms of real forest fire data.
Autonomous Driving System Design for Formula Racing
Authors: Zhang Zheng , Gao Peifeng , He Zilong , Chen Hongbin , Wang Jianfeng
Published: November 2020

This paper mainly introduces automatic driving schemes and algorithms for formula racing car. Formula racing is a good platform for testing the extreme performance of vehicles, but there is a risk of harming the drivers. We propose an automatic driving scheme to avoid such problems. The solution we proposed integrates three modules: environment perception, path planning and vehicle control. Using lidar, camera and GPS/INS integrated navigation as sensors, an innovative data fusion method is proposed. This paper proposes a new path planning method, which uses Delaunay triangulation and topological map-based path search and evaluation to obtain the best path. Finally, a control model based on pure pursuit algorithm is used to control the vehicle precisely. The whole system is developed based on ROS which has a unique loose coupling. This scheme is proved to have high accuracy and good robustness by simulation analysis and multiple vehicle tests. The proposed solution has good portability, and it can also be applied to the field of unmanned passenger cars, robots and drones after appropriate modifications.
A cherry tomato classification-picking Robot based on the K-means algorithm
Authors: Zhou Wenting , Meng Fanwen , Li Kun
Published: November 2020

In order to improve the automation level of cherry tomato harvesting, this paper proposes an cherry tomato picking robot. The robot includes four parts: a vision camera, a mechanical arm, a picking claw and a tracked AGV. The vision camera learns and classifies different varieties of cherry tomatoes based on the K-means algorithm. The color patch-based visual tracking algorithm accurately locates the ripe tomatoes, and the robotic arm drives the claws to pick them accurately. The tracked AGV adopts the original difference height intelligent tracking method, which has low cost and high adaptability to the working environment. It is verified by experiment that the cherry tomato classification-picking robot can effectively identify and locate different types of cherry tomatoes. The single-fruit tomato picking operation takes about 20s, the success rate is over 80%.
Multi-scale Feature Fusion for 3D Saliency Detection
Authors: Pan Gang , Wang An-Zhi , Xu Bao-Lei , Ou Weihua
Published: November 2020

3D saliency detection aims to take advantage of the disparity map, depth map and color information to automatically detect informative objects from natural scenes. Although studies have concentrated on this issue in recent years, there are challenges such as how to leverage disparity map or depth map effectively to compute depth-induced saliency, and how to fuse optimally multiple visual features and cues. A novel 3D saliency detection approach is proposed, which fuses local contrast, region contrast, texture feature, depth cue, and location cue into a unified saliency computation framework. Results show that the proposed approach achieves significant and consistent improvements on other advanced methods in the RGBD1000 datasets.
Image recognition algorithm based on spatial transform convolutional neural network
Authors: Xing Zhen-Zhen
Published: November 2020

Aiming at the problem of classification and recognition of noisy handwritten digits, a connection method is proposed to add a spatial transformation network to a convolutional neural network. The spatial transformation network can not only obtain the output results, but also understand the parts of the input data that have the greatest influence on the results and perform feature extraction, which can improve the performance of the model and enhance the interpretability of the model. The experiment was performed on the Cluttered MNIST dataset, and the effects of the conventional convolutional neural network and the improved neural network with spatial transformation network were compared. The experimental results show that the prediction accuracy of convolutional neural networks based on spatial transformation can reach 97.65%, and the results obtained by this method are better than those of conventional convolutional neural networks.
Research on multi-factor Identification model of acceptance content of Work order complaint
Authors: Huaiying Shang , Yan Liu , Zheng Angang , Qi Zhang , Min Ren
Published: November 2020

In this paper, combined with the power business demand, the purpose of this paper is to break the blind area of customers’ demand for electricity, so as to improve the management level of users’ electricity demand, and realize the mining of work order types of hot complaint business. In order to obtain the information behind the complaint work order, the power customer complaint work order is deeply mined. The weight value of keywords is calculated based on TF-IDF algorithm, and the text classifier model is constructed based on SVM classification algorithm. This study aims to provide differentiated service strategies for different types of power customers by controlling the main problems of current power customer complaints, so as to improve customer satisfaction and loyalty.
Application of IAFSA-BP neural network in face orientation recognition
Authors: Wang Hai-Jun , Jin Tao , Zhang Yi-Ting
Published: November 2020

In order to improve the recognition accuracy of BP neural network in face orientation recognition, an improved artificial fish swarm algorithm is proposed to optimize the weights and thresholds of BP neural network face orientation recognition model. The improved artificial fish swarm algorithm is based on the standard algorithm, introducing adaptive factors to make the horizon and step size adaptive change, and at the same time learning the solution before the result announcement, so as to improve the accuracy of the final result. Finally, the experimental results show that the effective combination of the improved artificial fish swarm algorithm and the BP neural network algorithm can improve the output accuracy of face orientation recognition of the BP neural network, and the running speed of the improved algorithm is significantly higher than that of the standard artificial fish swarm algorithm.
A lightweight convolutional neural network model for target recognition
Authors: He Chunqian , Li Dongsheng , Wang Siqi
Published: November 2020

Convolutional neural networks have achieved excellent performance in a wide range of applications, but the huge resource consumption makes a great challenge to their application on mobile terminals and embedded devices. In order to solve such problems, it is necessary to balance the size, speed and accuracy of the network model. This study proposed a new shallow neural network on the bases of ResNet and DenseNet. We use different size convolution kernels to obtain feature maps and then concat them. Afterwards we build two convolution layers to reduce the size of the feature maps and increase the depth of the network. By stacking this structure, we get our net model. Experiments show that our nine-layers network recognition performance is better than 18-layers ResNet and 19-layers DenseNet, and its training time is shorter. The final recognition rate of our network is 97.37%, ResNet recognition rate is 96.93%, and DenseNet is 96.31%.
Better Modeling the Hierarchical Structure of Language for Sentence Semantic Matching
Authors: Pei Zhongmin , Li Changqun , Xiong Wei , Luo Zhangkai
Published: November 2020

Sentence semantic matching requires understanding the semantic relationship between two sentences, which is widely used in various natural language tasks such as paraphrase identification, natural language inference and question answering. Usually, the underlying structure of sentence is not strictly sequential, this structure is hierarchical. This paper studies how to better model the hierarchical structure of sentence for semantic matching. Recent research suggests that BERT implicitly captures classical, tree-like structures. Based on this, this paper proposes to further enhance the strength of modeling hierarchical structure of language with an advanced variant of LSTM – Ordered Neurons LSTM(ON-LSTM), which introduces a syntax-oriented inductive bias to perform the composition of classical tree structure. Experimental results demonstrate that the proposed approach, which enhances hierarchical structure of language, significantly improves the performance of sentence semantic matching.
Pitch Angle: A Newfound Influential Trait for Image-Based Facial Beauty Perception
Authors: Guo Hao , Han Wenming , Chen Fangmei , Sun Fuming
Published: November 2020

One of the most crucial issues in facial beauty study is to find the traits, which determine the perceived attractiveness of a face. During the decades, many traits have been studied extensively, such as proportions, averageness, and symmetry. However, facial beauty seems to be more complex than these traits. In this paper, a face image is represented by a series of interpretable parameters, which are learnt by a CNN based 3DMM fitting algorithm. Based on the SCUT-FBP5500 dataset, we explore the relationship between those parameters and the human rated facial beauty scores. We observe that the pitch angle parameter has a strong correlation with the beauty score. In order to study the causal relationship between the pitch angle and facial beauty, face images with different pitch angles were generated as the stimuli and 39 volunteers were invited to a rating experiment. The results show that the change of the pitch angle has a significant effect on the perceived facial beauty. This finding may explain that head pose plays an important role in the self-portrait shot.
Evolvable Image Filter based on Rotation Invariant Pixel Correlations
Authors: Li Xiuling , Tu Ke , Jiang Decai , Wang Ting , Li Zhengdong
Published: November 2020

As one of the most typical nonlinear filters, weighted median filter has considerable filter behaviour by assigning weights. But each pixel has only one weight in the traditional weighted median filter. Correlations between pixel and other pixels under the filtering template are not considered. A novel rotation-invariant image filter model is proposed here which has a stable filtering performance when filtering on rotated and reversed images, symmetry of the correlations is utilized to reduce the complexity. Cartesian Genetic Programming is adopted to evolve image filter operators. Experiments about rotated images show that this rotation invariant model exhibits robust characteristics. Image denoising of different images show that it has better properties of edge or details preservation, efficient noise attenuation.
A staked discriminative auto-encoder based on center loss for radar target HRRP recognition
Authors: Li Rui , Wang Xiaodan , Quan Wen , Zhang Guoling , Xiang Qian
Published: November 2020

In the radar automatic target recognition filed, extracting representative features from the radar high resolution range profile (HRRP) is the key issues, which determines the accuracy and reliability of radar target recognition. In this paper, we propose a novel stacked discriminative auto-encoder(S-disAE), the center loss is integrated into auto-encoder, it can force the learned feature with large distance close to their class feature representation center, so as to reduce the intra-class variations while keeping the features of different classes separable. In pre-training stage of our discriminative auto-encoder(disAE), we combine the mean square error loss and center loss to learn the main factors of input raw data and minimize the intra-class variations of the feature, in fine-tuning stage, we also combine the softmax cross entropy loss and center loss to improve the classification accuracy. We conduct several experiments on the simulated HRRP data, the results demonstrate that the proposed algorithm can extract discriminant features and improve the HRRP recognition accuracy.
Summary of Object Recognition
Authors: Li Shiji , Chen Hong , Wang Qing , An Jiahui , Li Jiayue
Published: November 2020

Object recognition is one of the classic problems in computer vision. It is very important for computers to recognize the common objects in life like the human brain and the human eye. This is also an important step in the development of computers in the direction of intelligence. This article summarizes the current research on object recognition, and elaborates on several aspects of object feature extraction, object feature matching and object recognition methods.
Water System Segmentation Method of High Resolution Remote Sensing Image Based on eCognition
Authors: Xue Bei , Lin Xiaohu
Published: November 2020

High-resolution remote sensing images contain abundant ground object information. In this paper, water system elements are segmented and interpreted based on eCognition platform. Three segmentation methods, multi-scale image segmentation, quadtree segmentation and checkerboard segmentation, and their segmentation principles are introduced. The advantages and disadvantages of different segmentation methods are obtained through experimental analysis, and the optimal segmentation parameters of multi-scale segmentation are obtained by setting different segmentation parameter experiments. This paper is of guiding significance for the interpretation of water system data and the dynamic update of geographical survey elements in later remote sensing images.
A fast and robust real-time surveillance video stitching method
Authors: Yang Tao , Jin Fenlin , Luo Jianxin
Published: November 2020

Real-time video stitching can build a wider field of view for surveillance, which faces a compromise between stitching speed and visual quality. A fast and robust real-time surveillance video stitching method is proposed to deal with the ghosting effect caused by moving objects and misalignments caused by background change or slight camera shift through automatic updating. By stitching key frames, parameters such as pix mapping table, stitching seams and blending weights are calculated, and most of subsequent frames are directly blended with CUDA acceleration based on the pre-calculated stitching parameters. Fast and effective algorithms are designed to detect the change of stitching seam and background during the whole stitching process, which determines whether to update the stitching seam or recalculate stitching parameters. Experiments show that this method can robustly and automatically solve the ghosting and misalignments to improve visual quality and achieve satisfactory real-time performance.
Research on aircraft skin damage identification method based on image analysis
Authors: Du Hailong , Cao Yuanxiang
Published: November 2020

In the field of civil aviation maintenance, it is necessary to identify aircraft skin damage, and then formulate a maintenance plan. The traditional detection method process is more complicated. This paper proposes an image-based skin damage recognition method. After pre-processing the samples to form a unified sample library, the eigenvalues of the samples are obtained by wavelet packet decomposition and gray-level co-occurrence matrix, and finally the c and g values in the optimal RBF kernel function are selected to construct a support vector machine training model and the test results show that with a low sample size, the constructed model is more accurate in identifying normal skins and accidental impacts, and the overall system recognition rate is 81.5%.
A Modeling and Implementation Framework of Situation Deductive Information
Authors: Ni Hao , Zhong Zhinong , Li Jun , Wang Li
Published: November 2020

This paper mainly studies the modeling and implementation framework of deductive information in large-scale situation scenarios. In view of the deficiencies of the current plotting systems in dynamic deduction, we achieve the dynamic modeling of situation information by applying deductive motions to military symbols. And we propose a dynamic adjustment strategy of situation layers. At the same time, through timing control, deduction control and script control, the situation deductive process can meet the actual combat needs, which is convenient for analysis and research. Finally, through practical development, it proves that our deduction method is feasible, and solve the key technology of situation information expression and realization in battlefield situation deduction.
Wide spectrum denoising (WSD) for super-resolution microscopy imaging using compressed sensing and a high-resolution camera
Authors: Cheng Tao , Chen Danni , Li Heng
Published: November 2020

Because of the lack of effective denoising methods, any form of denoising is seldom performed for super-resolution microscopy, resulting in poor temporal and spatial resolutions. We propose a denoising method for STORM raw images based on compressed sensing and high-resolution cameras. This method overcomes the limitation that the raw pixel size must be approximately equal to the standard deviation of the point spread function. This method can be effectively used to remove random noise such as Poisson and Gaussian noise from very low density to ultra-high density fluorescent molecular distribution scenarios. Therefore, it is a wide spectrum denoising algorithm. Using this method, it was demonstrated that the SNR of a raw image can be increased by approximately 7 dB. Using CVX reconstruction, only 20 frames of the raw image are needed, and the time resolution is 0.86 s. The spatial resolution is also greatly improved.
Photo Content Classification Using Convolutional Neural Network
Authors: Hou Yilin
Published: November 2020

This research is about photo content classification using the TensorFlow framework and the deep neural network algorithm in deep learning. Machine learning is a multidisciplinary major whose main research object is artificial intelligence, which enables machines to have the learning ability like human beings. As a branch of machine learning, deep learning analyses and interprets texts, images, and sounds by learning the rules and representations of sample data. A convolutional neural network is an approach of a deep neural network, which is mostly applied to analyse the photo content and has good performance in photo content classification. It is also the main point that the study focuses on. The Visual Geometry Group 16 (VGG16) model in the convolutional neural network was used to achieve the research objectives and has excellent performance. The research used data from CelebFaces and Kaggle, including photos about people, food, animals, and architecture. The photo content classification model in this study can classify these four types of images. Moreover, the analysis of the work, summary, as well as the future work were provided at the end.
An Environmental Characterization Method for Monocular Vision Obstacle of Rotor Aircraft
Authors: Li Biao , Wu Wenzhen , Yang Xiaohui , Ji Desheng , Lu Xiaoyu , Cao Yasheng , Wang Xi
Published: November 2020

Combining the structure of coding and anti-coding in the field of deep learning, we explore a method to represent the complex environment of four rotor aircraft. First, we use this structure to explore the process of dimensionality reduction and reconstruction of high dimensional image data. Second, combining this structure, we use the concept of transposition convolution to propose a network structure for depth distance estimation using monocular vision images. Finally, we get the direct physical feature information used to describe complex environment.
Dense Attention Convolutional Network for Image Classification
Authors: Zhang Han , Qin Kun , Zhang Ye , Li Zhili , Xu Kai
Published: November 2020

Convolutional neural networks (CNN) have made rapid progress for a series of visual tasks, but the bottom-up convolutional feature extraction process is not qualified to mimic the human visual perception process, which takes advantages of discriminative features. Although attention modules extract the top-down discriminative features and have been widely investigated in the past few years, current attention modules interrupt the bottom-up convolutional feature extraction process. To tackle this challenge, in this paper, we introduce dense connection structure to fuse discriminative features from attention modules and convolutional features, which we term as dense attention learning. Also, to alleviate the over-fitting problem caused by rapid feature dimension growth, we propose a channel-wise attention module to compress and refine the convolutional features. Based on these strategies, we build a dense attention convolutional neural network (DA-CNN) for visual recognition. Exhaustive experiments on four challenging datasets including CIFAR-10, CIFAR-100, SVHN and ImageNet demonstrate that our DA-CNN outperforms many state-of-the-art methods. Moreover, the effectiveness of our dense attention learning and channel-wise attention module is also validated.
Tooth-Marked Tongue Recognition Based on Mask Scoring R-CNN
Authors: Kong Xiangyong , Rui Yingdi , Dong Xin , Cai Jian , Liu Yanan
Published: November 2020

Tooth-marked tongue is one of the important tongue features in Traditional Chinese Medicine tongue diagnosis. It reflects the dysfunction of liver, spleen and kidney, and contains rich pathological information. However, the recognition of tooth-marked tongue is challenging. Most existing methods only focus on the classification of tooth-marked tongue, and the exact location and number of teeth marks were not involved, which had no great practical significance for the subsequent treatment based on syndrome differentiation. In this paper, we try to solve these problems by proposing a method based on Mask Scoring R-CNN framework and transfer learning which can extract, visualize the teeth marks and identify the number of teeth marks. First, the tongue image is fed into residual network of depth 101 layers (ResNet-101) to generate regions of interest (RoIs) via region proposal network (RPN) and RoI feature via RoIAlign. Next, MaskIOU is then predicted using the predicted mask and RoI characteristics as inputs. The proposed approach achieved F1 score of 0.95. According to these experimental results, the approach can robustly detect and segment teeth marks, which can provide a basis for the severity analysis of tooth-marked tongue.
Parallax-Tolerant Image Stitching for Outdoor Scenes
Authors: Li Chunbin , Liu Jiahang
Published: November 2020

Image stitching aims to obtain a natural panorama without ghosts. This paper proposes a panoramic image stitching method for outdoor scenes. This method gets the new homography matrix by analyzing the spatial distribution of the feature points of the outdoor scene image, and then combines with the local registration to achieve precise registration. In order to generate a more natural panorama, this paper also combines the result of registration with local similarity transformation and seam-cutting processing. Finally, the experimental results show that the proposed method obtains better performance than other methods in image stitching of outdoor scenes.
Multi-feature fatigue driving detection based on computer vision
Authors: Huang Juan , Lin Zihui
Published: November 2020

Fatigue driving is one of the main causes of traffic accidents. This paper proposes a fatigue detection method based on computer vision. The first is the introduction of an optimized algorithm, based on AdaBoost, to detect the face area, and then the ERT algorithm is used to achieve precise localization of the facial landmarks. Finally, a variety of fatigue features of eyes and mouth state associated with driving fatigue are extracted, and after the fusion of all these features, the fatigue driving detection is performed. The experimental results show that multi-feature detection is more accurate than single feature detection.
Automatic music transcription based on convolutional neural network, constant Q transform and MFCC
Authors: Meng Zhihang , Chen Wencheng
Published: November 2020

Automatic music transcription is a function that relies on the efficiency of the computer’s own calculation speed to recognize the pitch of external input audio and output it accurately. In the past methods, there are often problems such as inability to reflect music continuity or narrow identifiable frequency range. In order to avoid such problems, two methods different from the previous Mel Cepstral Coefficients and Constant Q Transform are selected to extract the features of music, and convolutional neural networks are used for training and recognition. Among them, the Mel Cepstrum coefficient is used to judge the timbre, and the constant Q transform is used to judge the pitch. After inputting the corresponding features into the neural network for training and learning, we can find that the recognition success rate has reached 95%.
A Spatio-Temporal Attention Convolution Block for Action Recognition
Authors: Wang Junjie , Wen Xueyan
Published: November 2020

We propose a simple and effective 3D neural network module (STAT) embedded in spatiotemporal attention for action recognition. For a given intermediate feature map, our module sequentially infers the distribution of attention along the two dimensions of space and time, and multiplies it with the current feature map in the form of residual to achieve adaptive generation of the next stage feature map. STAT is a 3D convolution general module combined with attention. It is compatible with any 3D convolution network and can easily replace the 3D convolution kernel. The additional overhead it generates is negligible, and it can be trained end-to-end together with ordinary 3DCNN. By comparing the performance of the currently popular 3D networks on the UCF101 and HMDB51 datasets, experiments show that STAT has certain improvements on most 3D networks, which proves that STAT has a certain universality.
Research on Ultra-Wideband Radar Target Recognition Method
Authors: Li Junxia , Zhang Qin , Zheng Guimei
Published: November 2020

UWB (Ultra wide-brand) radar, as a new system radar, has attracted more and more attention in military and civil fields such as target detection, imaging and recognition with its high range resolution, strong penetration, low interception rate and strong anti-interference. This paper focuses on the feature extraction methods based on pole distribution and scattering center combined with the characteristics and application directions of UWB radar. On this basis, combined with intelligent pattern recognition methods such as neural networks, fuzzy patterns, and information fusion, the problems that still need to be solved in the process of UWB radar target recognition are proposed.