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 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 Super-resolution reconstruction of seismic section image via multi-scale convolution neural network

2021 ◽  
Vol 303 ◽  
pp. 01058
Author(s):  
Meng-Di Deng ◽  
Rui-Sheng Jia ◽  
Hong-Mei Sun ◽  
Xing-Li Zhang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Image Features ◽  
Image Feature ◽  
Reconstruction Method ◽  
Low Resolution ◽  
Seismic Section ◽  
Multi Scale

The resolution of seismic section images can directly affect the subsequent interpretation of seismic data. In order to improve the spatial resolution of low-resolution seismic section images, a super-resolution reconstruction method based on multi-scale convolution is proposed. This method designs a multi-scale convolutional neural network to learn high-low resolution image feature pairs, and realizes mapping learning from low-resolution seismic section images to high-resolution seismic section images. This multi-scale convolutional neural network model consists of four convolutional layers and a sub-pixel convolutional layer. Convolution operations are used to learn abundant seismic section image features, and sub-pixel convolution layer is used to reconstruct high-resolution seismic section image. The experimental results show that the proposed method is superior to the comparison method in peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In the total training time and reconstruction time, our method is about 22% less than the FSRCNN method and about 18% less than the ESPCN method.

scholarly journals High-Speed Ship Detection in SAR Images Based on a Grid Convolutional Neural Network

2019 ◽  
Vol 11 (10) ◽  
pp. 1206 ◽  
Author(s):  
Tianwen Zhang ◽  
Xiaoling Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
High Speed ◽  
Earth Observation ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Sar Images ◽  
Ship Detection ◽  
Detection Speed

As an active microwave sensor, synthetic aperture radar (SAR) has the characteristic of all-day and all-weather earth observation, which has become one of the most important means for high-resolution earth observation and global resource management. Ship detection in SAR images is also playing an increasingly important role in ocean observation and disaster relief. Nowadays, both traditional feature extraction methods and deep learning (DL) methods almost focus on improving ship detection accuracy, and the detection speed is neglected. However, the speed of SAR ship detection is extraordinarily significant, especially in real-time maritime rescue and emergency military decision-making. In order to solve this problem, this paper proposes a novel approach for high-speed ship detection in SAR images based on a grid convolutional neural network (G-CNN). This method improves the detection speed by meshing the input image, inspired by the basic thought of you only look once (YOLO), and using depthwise separable convolution. G-CNN is a brand new network structure proposed by us and it is mainly composed of a backbone convolutional neural network (B-CNN) and a detection convolutional neural network (D-CNN). First, SAR images to be detected are divided into grid cells and each grid cell is responsible for detection of specific ships. Then, the whole image is input into B-CNN to extract features. Finally, ship detection is completed in D-CNN under three scales. We experimented on an open SAR Ship Detection Dataset (SSDD) used by many other scholars and then validated the migration ability of G-CNN on two SAR images from RadarSat-1 and Gaofen-3. The experimental results show that the detection speed of our proposed method is faster than the existing other methods, such as faster-regions convolutional neural network (Faster R-CNN), single shot multi-box detector (SSD), and YOLO, under the same hardware environment with NVIDIA GTX1080 graphics processing unit (GPU) and the detection accuracy is kept within an acceptable range. Our proposed G-CNN ship detection system has great application values in real-time maritime disaster rescue and emergency military strategy formulation.

scholarly journals Automatic Lip-Reading System Based on Deep Convolutional Neural Network and Attention-Based Long Short-Term Memory

2019 ◽  
Vol 9 (8) ◽  
pp. 1599 ◽  
Author(s):  
Yuanyao Lu ◽  
Hongbo Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Image Features ◽  
Attention Mechanism ◽  
Image Feature ◽  
Fusion Model ◽  
Image Feature Extraction ◽  
Lip Reading

With the improvement of computer performance, virtual reality (VR) as a new way of visual operation and interaction method gives the automatic lip-reading technology based on visual features broad development prospects. In an immersive VR environment, the user’s state can be successfully captured through lip movements, thereby analyzing the user’s real-time thinking. Due to complex image processing, hard-to-train classifiers and long-term recognition processes, the traditional lip-reading recognition system is difficult to meet the requirements of practical applications. In this paper, the convolutional neural network (CNN) used to image feature extraction is combined with a recurrent neural network (RNN) based on attention mechanism for automatic lip-reading recognition. Our proposed method for automatic lip-reading recognition can be divided into three steps. Firstly, we extract keyframes from our own established independent database (English pronunciation of numbers from zero to nine by three males and three females). Then, we use the Visual Geometry Group (VGG) network to extract the lip image features. It is found that the image feature extraction results are fault-tolerant and effective. Finally, we compare two lip-reading models: (1) a fusion model with an attention mechanism and (2) a fusion model of two networks. The results show that the accuracy of the proposed model is 88.2% in the test dataset and 84.9% for the contrastive model. Therefore, our proposed method is superior to the traditional lip-reading recognition methods and the general neural networks.

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