scholarly journals Fake colorized and morphed image detection using convolutional neural network

2020 ◽  
Vol 6 (18) ◽  
pp. 8-16
Author(s):  
Neetu Pillai
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Detection

scholarly journals Terahertz Image Detection with the Improved Faster Region-Based Convolutional Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2327 ◽  
Author(s):  
Jinsong Zhang ◽  
Wenjie Xing ◽  
Mengdao Xing ◽  
Guangcai Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Terahertz Imaging ◽  
Image Detection ◽  
Data Format ◽  
Standard Data ◽  
Concealed Weapons ◽  
Special Distribution ◽  
Effectiveness And Efficiency

In recent years, terahertz imaging systems and techniques have been developed and have gradually become a leading frontier field. With the advantages of low radiation and clothing-penetrable, terahertz imaging technology has been widely used for the detection of concealed weapons or other contraband carried on personnel at airports and other secure locations. This paper aims to detect these concealed items with deep learning method for its well detection performance and real-time detection speed. Based on the analysis of the characteristics of terahertz images, an effective detection system is proposed in this paper. First, a lots of terahertz images are collected and labeled as the standard data format. Secondly, this paper establishes the terahertz classification dataset and proposes a classification method based on transfer learning. Then considering the special distribution of terahertz image, an improved faster region-based convolutional neural network (Faster R-CNN) method based on threshold segmentation is proposed for detecting human body and other objects independently. Finally, experimental results demonstrate the effectiveness and efficiency of the proposed method for terahertz image detection.

scholarly journals Image target detection algorithm compression and pruning based on neural network

2021 ◽  
Vol 18 (2) ◽  
pp. 499-516
Author(s):  
Yan Sun ◽  
Zheping Yan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Network Model ◽  
Detection Algorithm ◽  
Image Feature ◽  
Propagation Mode ◽  
Learning Technology ◽  
Image Detection ◽  
Detection Algorithms

The main purpose of target detection is to identify and locate targets from still images or video sequences. It is one of the key tasks in the field of computer vision. With the continuous breakthrough of deep machine learning technology, especially the convolutional neural network model shows strong Ability to extract image feature in the field of digital image processing. Although the model research of target detection based on convolutional neural network is developing rapidly, but there are still some problems in practical applications. For example, a large number of parameters requires high storage and computational costs in detected model. Therefore, this paper optimizes and compresses some algorithms by using early image detection algorithms and image detection algorithms based on convolutional neural networks. After training and learning, there will appear forward propagation mode in the application of CNN network model, providing the model for image feature extraction, integration processing and feature mapping. The use of back propagation makes the CNN network model have the ability to optimize learning and compressed algorithm. Then research discuss the Faster-RCNN algorithm and the YOLO algorithm. Aiming at the problem of the candidate frame is not significant which extracted in the Faster- RCNN algorithm, a target detection model based on the Significant area recommendation network is proposed. The weight of the feature map is calculated by the model, which enhances the saliency of the feature and reduces the background interference. Experiments show that the image detection algorithm based on compressed neural network image has certain feasibility.

scholarly journals Detection of ship targets in photoelectric images based on an improved recurrent attention convolutional neural network

2020 ◽  
Vol 16 (3) ◽  
pp. 155014772091295 ◽  
Author(s):  
Zhijing Xu ◽  
Yuhao Huo ◽  
Kun Liu ◽  
Sidong Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Feature ◽  
Detection Accuracy ◽  
Image Detection ◽  
Image Feature Extraction ◽  
Ship Detection ◽  
Multi Scale ◽  
Network Methods ◽  
Accelerate Convergence

Deep learning algorithms have been increasingly used in ship image detection and classification. To improve the ship detection and classification in photoelectric images, an improved recurrent attention convolutional neural network is proposed. The proposed network has a multi-scale architecture and consists of three cascading sub-networks, each with a VGG19 network for image feature extraction and an attention proposal network for locating feature area. A scale-dependent pooling algorithm is designed to select an appropriate convolution in the VGG19 network for classification, and a multi-feature mechanism is introduced in attention proposal network to describe the feature regions. The VGG19 and attention proposal network are cross-trained to accelerate convergence and to improve detection accuracy. The proposed method is trained and validated on a self-built ship database and effectively improve the detection accuracy to 86.7% outperforming the baseline VGG19 and recurrent attention convolutional neural network methods.

scholarly journals REPLICATION IMAGE DETECTION USING CONVOLUTIONAL NEURAL NETWORK

2021 ◽  
Author(s):  
K. Kalaiselvi ◽  
◽  
S. Saranya ◽  
K. Deepa Thilak ◽  
K. Kumaresan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Conventional Method ◽  
Medical Images ◽  
Image Data ◽  
Image Detection ◽  
Convolution Model ◽  
Ease Of Access

The ease of access to image data has led to overuse of repeated images at various instances which leads to increases duplication and redundancy in many industries. Advanced editing techniques which are available very easily, encourages original copyrights images to be misused. This results in lack of originality in data generated at every level. Common solutions include allowing manual selections of duplicate images or compares images pixel by pixel. The conventional method is to use 3 branch Siamese Convolution model to detect duplication of medical images. We propose to develop a model to detect duplication in everyday images by training a Siamese Convolutional Neural Network and try to achieve greater accuracy than previously developed solutions. Using Grad-Cam network inspection we propose to inspect the decisions taken by the CNN upon detecting duplication in images.

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