scholarly journals Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 210 ◽  
Author(s):  
Zhuofan Yan ◽  
Jinsong Chong ◽  
Yawei Zhao ◽  
Kai Sun ◽  
Yuhang Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Detection Method ◽  
Synthetic Aperture ◽  
Detection Methods ◽  
Sar Images ◽  
Detection Thresholds ◽  
Generalization Ability ◽  
Spatial Pyramid Pooling ◽  
Spatial Pyramid

Oceanic phenomena detection in synthetic aperture radar (SAR) images is important in the fields of fishery, military, and oceanography. The traditional detection methods of oceanic phenomena in SAR images are based on handcrafted features and detection thresholds, which have a problem of poor generalization ability. Methods based on deep learning have good generalization ability. However, most of the deep learning methods currently applied to oceanic phenomena detection only detect one type of phenomenon. To satisfy the requirements of efficient and accurate detection of multiple information of multiple oceanic phenomena in massive SAR images, this paper proposes an oceanic phenomena detection method in SAR images based on convolutional neural network (CNN). The method first uses ResNet-50 to extract multilevel features. Second, it uses the atrous spatial pyramid pooling (ASPP) module to extract multiscale features. Finally, it fuses multilevel features and multiscale features to detect oceanic phenomena. The SAR images acquired from the Sentinel-1 satellite are used to establish a sample dataset of oceanic phenomena. The method proposed can achieve 91% accuracy on the dataset.

scholarly journals Patch-Based Change Detection Method for SAR Images with Label Updating Strategy

2021 ◽  
Vol 13 (7) ◽  
pp. 1236
Author(s):  
Yuanjun Shu ◽  
Wei Li ◽  
Menglong Yang ◽  
Peng Cheng ◽  
Songchen Han
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Change Detection ◽  
Detection Method ◽  
Training Data ◽  
Synthetic Aperture ◽  
Learning Method ◽  
Sar Images ◽  
Two Stage ◽  
Second Stage

Convolutional neural networks (CNNs) have been widely used in change detection of synthetic aperture radar (SAR) images and have been proven to have better precision than traditional methods. A two-stage patch-based deep learning method with a label updating strategy is proposed in this paper. The initial label and mask are generated at the pre-classification stage. Then a two-stage updating strategy is applied to gradually recover changed areas. At the first stage, diversity of training data is gradually restored. The output of the designed CNN network is further processed to generate a new label and a new mask for the following learning iteration. As the diversity of data is ensured after the first stage, pixels within uncertain areas can be easily classified at the second stage. Experiment results on several representative datasets show the effectiveness of our proposed method compared with several existing competitive methods.

scholarly journals A Method for Wheat Head Detection Based on Yolov4

2020 ◽  
Author(s):  
Bo Gong ◽  
Daji Ergu ◽  
Ying Cai ◽  
Bo Ma
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Detection Method ◽  
Computer Hardware ◽  
Detection Methods ◽  
Plant Phenotyping ◽  
Wide Range ◽  
Head Detection ◽  
Multi Level

Abstract Background: Plant phenotyping by deep learning has increased attention. The detection of wheat head in the field is an important mission for estimating the characteristics of wheat heads such as the density, health, maturity, and presence or absence of awns. Traditional wheat head detection methods have problems such as low efficiency, strong subjectivity, and poor accuracy. However, with the development of deep learning theory and the iteration of computer hardware, the accuracy of object detection method using deep neural networks has been greatly improved. Therefore, using a deep neural network method to detect wheat heads in images has a certain value. Results: In this paper, a method of wheat head detection based on deep neural network is proposed. Firstly, for improving the backbone network part, two SPP networks are introduced to enhance the ability of feature learning and increase the receptive field of the convolutional network. Secondly, the top-down and bottom-up feature fusion strategies are applied to obtain multi-level features. Finally, we use Yolov3's head structures to predict the bounding box of object. The results show that our proposed detection method for wheat head has higher accuracy and speed. The mean average precision of our method is 94.5%, and the detection speed of our proposed method is 88fps. Conclusion: The proposed deep neural network can accurately and quickly detector the wheat head in the image which is based on Yolov4. In addition, the training dataset is a wheat head dataset with accurate annotations and rich varieties, which makes the proposed method more robust and has a wide range of application values. The proposed detector is also more suitable for wheat detection task, with the deeper backbone networks. The use of spatial pyramid pooling (SPP) and multi-level features fusion, which all play a crucial role in improving detector performance. Our method provides beneficial help for the breeding of wheat

scholarly journals Automatic Handgun Detection with Deep Learning in Video Surveillance Images

2021 ◽  
Vol 11 (13) ◽  
pp. 6085
Author(s):  
Jesus Salido ◽  
Vanesa Lomas ◽  
Jesus Ruiz-Santaquiteria ◽  
Oscar Deniz
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Video Surveillance ◽  
Automatic Detection ◽  
Public Spaces ◽  
Detection Methods ◽  
Training Dataset ◽  
Average Precision ◽  
Terrorist Acts

There is a great need to implement preventive mechanisms against shootings and terrorist acts in public spaces with a large influx of people. While surveillance cameras have become common, the need for monitoring 24/7 and real-time response requires automatic detection methods. This paper presents a study based on three convolutional neural network (CNN) models applied to the automatic detection of handguns in video surveillance images. It aims to investigate the reduction of false positives by including pose information associated with the way the handguns are held in the images belonging to the training dataset. The results highlighted the best average precision (96.36%) and recall (97.23%) obtained by RetinaNet fine-tuned with the unfrozen ResNet-50 backbone and the best precision (96.23%) and F1 score values (93.36%) obtained by YOLOv3 when it was trained on the dataset including pose information. This last architecture was the only one that showed a consistent improvement—around 2%—when pose information was expressly considered during training.

Speckle Noise Filtering Using Back-Propagation Multi-Layer Perceptron Network in Synthetic Aperture Radar Image

2020 ◽  
pp. 489-510
Author(s):  
Khwairakpam Amitab ◽  
Debdatta Kandar ◽  
Arnab K. Maji
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Synthetic Aperture Radar ◽  
Back Propagation ◽  
Speckle Noise ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Multi Layer Perceptron ◽  
Sar Images ◽  
Artificial Neural

Synthetic Aperture Radar (SAR) are imaging Radar, it uses electromagnetic radiation to illuminate the scanned surface and produce high resolution images in all-weather condition, day and night. Interference of signals causes noise and degrades the quality of the image, it causes serious difficulty in analyzing the images. Speckle is multiplicative noise that inherently exist in SAR images. Artificial Neural Network (ANN) have the capability of learning and is gaining popularity in SAR image processing. Multi-Layer Perceptron (MLP) is a feed forward artificial neural network model that consists of an input layer, several hidden layers, and an output layer. We have simulated MLP with two hidden layer in Matlab. Speckle noises were added to the target SAR image and applied MLP for speckle noise reduction. It is found that speckle noise in SAR images can be reduced by using MLP. We have considered Log-sigmoid, Tan-Sigmoid and Linear Transfer Function for the hidden layers. The MLP network are trained using Gradient descent with momentum back propagation, Resilient back propagation and Levenberg-Marquardt back propagation and comparatively evaluated the performance.

scholarly journals Oil Spill Detection in Quad-Polarimetric SAR Images Using an Advanced Convolutional Neural Network Based on SuperPixel Model

2020 ◽  
Vol 12 (6) ◽  
pp. 944 ◽  
Author(s):  
Jin Zhang ◽  
Hao Feng ◽  
Qingli Luo ◽  
Yu Li ◽  
Jujie Wei ◽  
...  
Keyword(s):  
Neural Network ◽  
Synthetic Aperture Radar ◽  
Convolutional Neural Network ◽  
Oil Spill ◽  
Classification Accuracy ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Feature Sets ◽  
Oil Spill Detection ◽  
Component Decomposition

Oil spill detection plays an important role in marine environment protection. Quad-polarimetric Synthetic Aperture Radar (SAR) has been proved to have great potential for this task, and different SAR polarimetric features have the advantages to recognize oil spill areas from other look-alikes. In this paper we proposed an oil spill detection method based on convolutional neural network (CNN) and Simple Linear Iterative Clustering (SLIC) superpixel. Experiments were conducted on three Single Look Complex (SLC) quad-polarimetric SAR images obtained by Radarsat-2 and Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR). Several groups of polarized parameters, including H/A/Alpha decomposition, Single-Bounce Eigenvalue Relative Difference (SERD), correlation coefficients, conformity coefficients, Freeman 3-component decomposition, Yamaguchi 4-component decomposition were extracted as feature sets. Among all considered polarimetric features, Yamaguchi parameters achieved the highest performance with total Mean Intersection over Union (MIoU) of 90.5%. It is proved that the SLIC superpixel method significantly improved the oil spill classification accuracy on all the polarimetric feature sets. The classification accuracy of all kinds of targets types were improved, and the largest increase on mean MIoU of all features sets was on emulsions by 21.9%.

scholarly journals COVID-19 Rumor Detection on Social Networks Based on Content Information and User Response

2021 ◽  
Vol 9 ◽  
Author(s):  
Jianliang Yang ◽  
Yuchen Pan
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Detection Method ◽  
Detection Methods ◽  
Time Constraints ◽  
Human Society ◽  
Limited Time ◽  
Response Information ◽  
Rumor Detection ◽  
Content Information

The outbreak of COVID-19 has caused a huge shock for human society. As people experience the attack of the COVID-19 virus, they also are experiencing an information epidemic at the same time. Rumors about COVID-19 have caused severe panic and anxiety. Misinformation has even undermined epidemic prevention to some extent and exacerbated the epidemic. Social networks have allowed COVID-19 rumors to spread unchecked. Removing rumors could protect people’s health by reducing people’s anxiety and wrong behavior caused by the misinformation. Therefore, it is necessary to research COVID-19 rumor detection on social networks. Due to the development of deep learning, existing studies have proposed rumor detection methods from different perspectives. However, not all of these approaches could address COVID-19 rumor detection. COVID-19 rumors are more severe and profoundly influenced, and there are stricter time constraints on COVID-19 rumor detection. Therefore, this study proposed and verified the rumor detection method based on the content and user responses in limited time CR-LSTM-BE. The experimental results show that the performance of our approach is significantly improved compared with the existing baseline methods. User response information can effectively enhance COVID-19 rumor detection.

scholarly journals FruitDet: Attentive Feature Aggregation for Real-Time Fruit Detection in Orchards

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2440
Author(s):  
Faris A. Kateb ◽  
Muhammad Mostafa Monowar ◽  
Md. Abdul Hamid ◽  
Abu Quwsar Ohi ◽  
Muhammad Firoz Mridha
Keyword(s):  
Computer Vision ◽  
Learning Strategies ◽  
Precision Agriculture ◽  
Detection System ◽  
Detection Methods ◽  
High Complexity ◽  
Detection Mechanism ◽  
Detection Model ◽  
Spatial Pyramid Pooling ◽  
Spatial Pyramid

Computer vision is currently experiencing success in various domains due to the harnessing of deep learning strategies. In the case of precision agriculture, computer vision is being investigated for detecting fruits from orchards. However, such strategies limit too-high complexity computation that is impossible to embed in an automated device. Nevertheless, most investigation of fruit detection is limited to a single fruit, resulting in the necessity of a one-to-many object detection system. This paper introduces a generic detection mechanism named FruitDet, designed to be prominent for detecting fruits. The FruitDet architecture is designed on the YOLO pipeline and achieves better performance in detecting fruits than any other detection model. The backbone of the detection model is implemented using DenseNet architecture. Further, the FruitDet is packed with newer concepts: attentive pooling, bottleneck spatial pyramid pooling, and blackout mechanism. The detection mechanism is benchmarked using five datasets, which combines a total of eight different fruit classes. The FruitDet architecture acquires better performance than any other recognized detection methods in fruit detection.

scholarly journals Employing GRU to combine feature maps in DeeplabV3 for a better segmentation model

2021 ◽  
Vol 1 (1) ◽  
pp. 29-31
Author(s):  
Mahmood Haithami ◽  
Amr Ahmed ◽  
Iman Yi Liao ◽  
Hamid Jalab
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Feature Maps ◽  
Feature Map ◽  
Input Feature ◽  
Spatial Pyramid Pooling ◽  
Test Sets ◽  
Public Datasets ◽  
Spatial Pyramid

In this paper, we aim to enhance the segmentation capabilities of DeeplabV3 by employing Gated Recurrent Neural Network (GRU). A 1-by-1 convolution in DeeplabV3 was replaced by GRU after the Atrous Spatial Pyramid Pooling (ASSP) layer to combine the input feature maps. The convolution and GRU have sharable parameters, though, the latter has gates that enable/disable the contribution of each input feature map. The experiments on unseen test sets demonstrate that employing GRU instead of convolution would produce better segmentation results. The used datasets are public datasets provided by MedAI competition.

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