scholarly journals Research status of damage identification algorithm based on deep learning

2021 ◽  
Vol 233 ◽  
pp. 04039
Author(s):  
Zhu Denghui ◽  
Song Lizhong ◽  
Feng yuan ◽  
Yang Quanshun
Keyword(s):  
Deep Learning ◽  
Target Detection ◽  
Damage Identification ◽  
Rapid Development ◽  
Research Progress ◽  
Data Sets ◽  
Identification Algorithm ◽  
Advantages And Disadvantages ◽  
Detection Technology ◽  
Application Fields

One of the core tasks of computer vision is target detection. With the rapid development of deep learning, target detection technology based on deep learning has become the mainstream algorithm in this field. As one of the main application fields, damage identification has achieved important development in the past decade. This paper systematically summarizes the research progress of damage identification algorithm based on deep learning, analyzes the advantages and disadvantages of each algorithm and its detection results on voc2007, voc2012 and coco data sets. Finally, the main contents of this paper are summarized, and the research prospect of deep learning based damage identification algorithm is prospect.

scholarly journals A Review of Intelligent Driving Pedestrian Detection Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Di Tian ◽  
Yi Han ◽  
Biyao Wang ◽  
Tian Guan ◽  
Wei Wei
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Rapid Development ◽  
Pedestrian Detection ◽  
Evaluation Criteria ◽  
Human Perception ◽  
Detection Accuracy ◽  
Learning Stage ◽  
Detection Technology ◽  
General Object

Pedestrian detection is a specific application of object detection. Compared with general object detection, it shows similarities and unique characteristics. In addition, it has important application value in the fields of intelligent driving and security monitoring. In recent years, with the rapid development of deep learning, pedestrian detection technology has also made great progress. However, there still exists a huge gap between it and human perception. Meanwhile, there are still a lot of problems, and there remains a lot of room for research. Regarding the application of pedestrian detection in intelligent driving technology, it is of necessity to ensure its real-time performance. Additionally, it is necessary to lighten the model while ensuring detection accuracy. This paper first briefly describes the development process of pedestrian detection and then concentrates on summarizing the research results of pedestrian detection technology in the deep learning stage. Subsequently, by summarizing the pedestrian detection dataset and evaluation criteria, the core issues of the current development of pedestrian detection are analyzed. Finally, the next possible development direction of pedestrian detection technology is explained at the end of the paper.

scholarly journals Research on Network Security Application Based on Deep Learning

CONVERTER ◽  
2021 ◽  
pp. 598-605
Author(s):  
Zhao Jianchao
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Internet Security ◽  
Detection Accuracy ◽  
Data Sets ◽  
Learning Technology ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Internet Industry

Behind the rapid development of the Internet industry, Internet security has become a hidden danger. In recent years, the outstanding performance of deep learning in classification and behavior prediction based on massive data makes people begin to study how to use deep learning technology. Therefore, this paper attempts to apply deep learning to intrusion detection to learn and classify network attacks. Aiming at the nsl-kdd data set, this paper first uses the traditional classification methods and several different deep learning algorithms for learning classification. This paper deeply analyzes the correlation among data sets, algorithm characteristics and experimental classification results, and finds out the deep learning algorithm which is relatively good at. Then, a normalized coding algorithm is proposed. The experimental results show that the algorithm can improve the detection accuracy and reduce the false alarm rate.

High resolution remote sensing image ship target detection technology based on deep learning

2019 ◽  
Vol 15 (5) ◽  
pp. 391-395 ◽  
Author(s):  
Min Wang ◽  
Jin-yong Chen ◽  
Gang Wang ◽  
Feng Gao ◽  
Kang Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Target Detection ◽  
Remote Sensing Image ◽  
Detection Technology

scholarly journals Monitoring Method of Transmission Line Breaking Prevention Based on Deep Learning

2021 ◽  
Vol 252 ◽  
pp. 01024
Author(s):  
Jiang Yan ◽  
Li Qiang ◽  
Wang Guanyao ◽  
Wang Ben ◽  
Deng Wei
Keyword(s):  
Deep Learning ◽  
Transmission Line ◽  
Transmission Lines ◽  
Rapid Development ◽  
Video Data ◽  
Data Sets ◽  
Monitoring Method ◽  
Monitoring Model ◽  
Public Data ◽  
3D Cnn

With the rapid development of the national economy, the national power consumption level continues to increase, which puts forward higher requirements on the power supply guarantee capacity of the power grid system. The distribution range of the transmission line is wide and densely, most lines are exposed to the unguarded field without any shielding or protective measures, which are vulnerable to man-made destruction or natural disasters. Therefore, it is very important for the early monitoring and prevention of the external force breaking of the transmission lines. The method for preventing external breakage of transmission lines based on deep learning proposed in this paper utilizes the video data collected by the cameras erected on the transmission line roads to perform feature extraction and learning through 3D CNN and LSTM networks, and obtains a monitoring model for external breakage prevention of transmission lines. The model was tested on public data sets and verified that it has a good performance in the field of transmission lines against external damage. The method in this paper makes full use of the existing video acquisition equipment, and the process does not require human intervention, which greatly reduces the cost of line monitoring and the hidden dangers of accidents.

scholarly journals Small-Sized Ship Detection Nearshore Based on Lightweight Active Learning Model with a Small Number of Labeled Data for SAR Imagery

2021 ◽  
Vol 13 (17) ◽  
pp. 3400
Author(s):  
Xiaomeng Geng ◽  
Lingli Zhao ◽  
Lei Shi ◽  
Jie Yang ◽  
Pingxiang Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Active Learning ◽  
Target Detection ◽  
Learning Strategy ◽  
Rapid Development ◽  
False Alarms ◽  
Ship Detection ◽  
Candidate Target ◽  
Artificial Intelligence Technology ◽  
Sar Imagery

Marine ship detection by synthetic aperture radar (SAR) is an important remote sensing technology. The rapid development of big data and artificial intelligence technology has facilitated the wide use of deep learning methods in SAR imagery for ship detection. Although deep learning can achieve a much better detection performance than traditional methods, it is difficult to achieve satisfying performance for small-sized ships nearshore due to the weak scattering caused by their material and simple structure. Another difficulty is that a huge amount of data needs to be manually labeled to obtain a reliable CNN model. Manual labeling each datum not only takes too much time but also requires a high degree of professional knowledge. In addition, the land and island with high backscattering often cause high false alarms for ship detection in the nearshore area. In this study, a novel method based on candidate target detection, boundary box optimization, and convolutional neural network (CNN) embedded with active learning strategy is proposed to improve the accuracy and efficiency of ship detection in nearshore areas. The candidate target detection results are obtained by global threshold segmentation. Then, the strategy of boundary box optimization is defined and applied to reduce the noise and false alarms caused by island and land targets as well as by sidelobe interference. Finally, a lightweight CNN embedded with active learning scheme is used to classify the ships using only a small labeled training set. Experimental results show that the performance of the proposed method for small-sized ship detection can achieve 97.78% accuracy and 0.96 F1-score with Sentinel-1 images in complex nearshore areas.

scholarly journals Research Progress of Rotating Machinery Fault Diagnosis Based on Deep Learning

2021 ◽  
Vol 257 ◽  
pp. 02030
Author(s):  
Zhehua Du ◽  
Xin Lin
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
High Speed ◽  
Large Scale ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Research Progress ◽  
Engineering Practice ◽  
Advantages And Disadvantages ◽  
Deep Learning Model

In modern production, the precision and the importance of rotating machinery is higher and higher in the direction of large-scale, high speed and automation development, so that the traditional fault diagnosis methods are insufficient to deal with massive, multi-source and high-dimensional data, cannot meet the requirements of security and reliability. Therefore, several typical deep learning models are briefly introduced at first and the application of deep learning in fault diagnosis of rotor system, gear box and rolling bearing in recent years is studied and analyzed based on its strong feature extraction ability and advantages of clustering analysis. Finally, the advantages and disadvantages of deep learning model are summarized and the fault diagnosis methods of rotating machinery are summarized and prospected based on engineering practice.

An overview of complex data stream ensemble classification

2021 ◽  
pp. 1-29
Author(s):  
Xilong Zhang ◽  
Meng Han ◽  
Hongxin Wu ◽  
Muhang Li ◽  
Zhiqiang Chen
Keyword(s):  
Data Streams ◽  
Concept Drift ◽  
Rapid Development ◽  
Complex Structure ◽  
Classification Performance ◽  
Ensemble Classification ◽  
Future Research ◽  
Complex Data ◽  
Advantages And Disadvantages ◽  
Application Fields

With the rapid development of information technology, data streams in various fields are showing the characteristics of rapid arrival, complex structure and timely processing. Complex types of data streams make the classification performance worse. However, ensemble classification has become one of the main methods of processing data streams. Ensemble classification performance is better than traditional single classifiers. This article introduces the ensemble classification algorithms of complex data streams for the first time. Then overview analyzes the advantages and disadvantages of these algorithms for steady-state, concept drift, imbalanced, multi-label and multi-instance data streams. At the same time, the application fields of data streams are also introduced which summarizes the ensemble algorithms processing text, graph and big data streams. Moreover, it comprehensively summarizes the verification technology, evaluation indicators and open source platforms of complex data streams mining algorithms. Finally, the challenges and future research directions of ensemble learning algorithms dealing with uncertain, multi-type, delayed, multi-type concept drift data streams are given.

scholarly journals Study on a Risk Model and Target Detection for Prediction and Avoidance of Unmanned Environmental Hazard

2021 ◽  
Author(s):  
Chengqun Qiu ◽  
Yuan Zhong ◽  
Jie Ji ◽  
Shuai Zhang ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Prediction ◽  
Target Detection ◽  
Environmental Risk ◽  
Risk Model ◽  
Learning Algorithm ◽  
Unmanned Vehicles ◽  
Deep Learning Algorithm ◽  
Detection Technology ◽  
Comparison Results

Abstract Comprehensive research is conducted on the design and control of the unmanned systems for electric vehicles. The environmental risk prediction and avoidance system is divided into the prediction part and the avoidance part. The prediction part is divided into environmental perception, environmental risk assessment, and risk prediction. In the avoidance part, the conservative driving strategy based on speed restriction is adopted according to the results of risk prediction. Additionally, the core function is achieved through the target detection technology based on deep learning algorithm and the data conclusion based on deep learning method. Moreover, the location of bounding box is further optimized to improve the accuracy of SSD target detection method based on solving the problem of unbalanced sample categories. Software such as MATLAB and Carsim are applied in the system. From the comparison results of the simulations of unmanned vehicles with or without a system, it that the system can provide effective safety guarantee for unmanned driving.

scholarly journals A Review of Intrusion Detection Technology Based on Deep Rein-forcement Learning

2021 ◽  
Vol 7 (4) ◽  
pp. 104
Author(s):  
Juquan Yu ◽  
Rui Zhou ◽  
Ziming Wang ◽  
Zixing Wang
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Defense Mechanism ◽  
Rapid Development ◽  
Modern Science ◽  
Network Intrusion Detection ◽  
Extensive Investigation ◽  
Network Attacks ◽  
Network Intrusion ◽  
Detection Technology

<p>With the rapid development of modern science and technology, all kinds of network attacks are updated constantly. Therefore, the traditional network security defense mechanism needs to be further improved. Through extensive investigation, this paper presents the latest work of network intrusion detection technology based on deep learning. Firstly, this paper introduces the related concepts of network intrusion detection technology. On this basis, we further evaluate the performance of three common deep learning models in intrusion detection, and conclude that DBN algorithm has some strong advantages. Afterwards, it also puts forward several improvement strategies of intrusion detection models.</p>

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