Video Object Forgery Detection Algorithm Based on VGG-11 Convolutional Neural Network

2019 ◽  
Author(s):  
Yanfen Gan ◽  
Jixiang Yang ◽  
Wenda Lai
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection Algorithm ◽  
Video Object ◽  
Forgery Detection

scholarly journals A Detection algorithm based on Convolutional Neural Network

2018 ◽  
Author(s):  
Fei Rong ◽  
Li Shasha ◽  
Xu Qingzheng ◽  
Liu Kun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Training Model ◽  
Test Sample ◽  
Training Sample ◽  
Detection Algorithm ◽  
Small Scale ◽  
Detection Model ◽  
Sample Data ◽  
Ratio Change

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

Loop Closure Detection Algorithm Based On Multi-Level Convolutional Neural Network Features

2018 ◽  
Vol 55 (11) ◽  
pp. 111507
Author(s):  
鲍振强 Bao Zhenqiang ◽  
李艾华 Li Aihua ◽  
崔智高 Cui Zhigao ◽  
苏延召 Su Yanzhao ◽  
郑勇 Zheng Yong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection Algorithm ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Multi Level

scholarly journals 3D Object Detection Algorithm for Panoramic Images With Multi-Scale Convolutional Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 171461-171470
Author(s):  
Dianwei Wang ◽  
Yanhui He ◽  
Ying Liu ◽  
Daxiang Li ◽  
Shiqian Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection Algorithm ◽  
3D Object ◽  
Multi Scale ◽  
Panoramic Images ◽  
3D Object Detection

scholarly journals Learning to Detect Cracks on Damaged Concrete Surfaces Using Two-Branched Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4796
Author(s):  
Jieun Lee ◽  
Hee-Sun Kim ◽  
Nayoung Kim ◽  
Eun-Mi Ryu ◽  
Je-Won Kang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real World ◽  
Crack Detection ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Image Sensors ◽  
Final Output ◽  
Gradient Component ◽  
Functional Components

Image sensors are widely used for detecting cracks on concrete surfaces to help proactive and timely management of concrete structures. However, it is a challenging task to reliably detect cracks on damaged surfaces in the real world due to noise and undesired artifacts. In this paper, we propose an autonomous crack detection algorithm based on convolutional neural network (CNN) to solve the problem. To this aim, the proposed algorithm uses a two-branched CNN architecture, consisting of sub-networks named a crack-component-aware (CCA) network and a crack-region-aware (CRA) network. The CCA network is to learn gradient component regarding cracks, and the CRA network is to learn a region-of-interest by distinguishing critical cracks and noise such as scratches. Specifically, the two sub-networks are built on convolution-deconvolution CNN architectures, but also they are comprised of different functional components to achieve their own goals efficiently. The two sub-networks are trained in an end-to-end to jointly optimize parameters and produce the final output of localizing important cracks. Various crack image samples and learning methods are used for efficiently training the proposed network. In the experimental results, the proposed algorithm provides better performance in the crack detection than the conventional algorithms.

Bidirectional IndRNN malicious webpages detection algorithm based on convolutional neural network and attention mechanism

2020 ◽  
Vol 38 (2) ◽  
pp. 1929-1941 ◽  
Author(s):  
Huan-Huan Wang ◽  
Sheng-Wei Tian ◽  
Long Yu ◽  
Xian-Xian Wang ◽  
Qing-Shan Qi ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection Algorithm ◽  
Attention Mechanism

scholarly journals SCNN: A General Distribution Based Statistical Convolutional Neural Network with Application to Video Object Detection

2019 ◽  
Vol 33 ◽  
pp. 5321-5328 ◽  
Author(s):  
Tianchen Wang ◽  
Jinjun Xiong ◽  
Xiaowei Xu ◽  
Yiyu Shi
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Correlated Data ◽  
General Distribution ◽  
Video Object ◽  
Human Beings ◽  
Detection And Tracking ◽  
General Network ◽  
Multiple Frames

Various convolutional neural networks (CNNs) were developed recently that achieved accuracy comparable with that of human beings in computer vision tasks such as image recognition, object detection and tracking, etc. Most of these networks, however, process one single frame of image at a time, and may not fully utilize the temporal and contextual correlation typically present in multiple channels of the same image or adjacent frames from a video, thus limiting the achievable throughput. This limitation stems from the fact that existing CNNs operate on deterministic numbers. In this paper, we propose a novel statistical convolutional neural network (SCNN), which extends existing CNN architectures but operates directly on correlated distributions rather than deterministic numbers. By introducing a parameterized canonical model to model correlated data and defining corresponding operations as required for CNN training and inference, we show that SCNN can process multiple frames of correlated images effectively, hence achieving significant speedup over existing CNN models. We use a CNN based video object detection as an example to illustrate the usefulness of the proposed SCNN as a general network model. Experimental results show that even a nonoptimized implementation of SCNN can still achieve 178% speedup over existing CNNs with slight accuracy degradation.

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