scholarly journals DEFORESTATION DETECTION IN THE AMAZON RAINFOREST WITH SPATIAL AND CHANNEL ATTENTION MECHANISMS

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 851-858
Author(s):  
P. Tovar ◽  
M. O. Adarme ◽  
R. Q. Feitosa
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Loss Function ◽  
Satellite Images ◽  
Attention Mechanism ◽  
Amazon Rainforest ◽  
Average Precision ◽  
Manual Task ◽  
Optical Images ◽  
The Mean

Abstract. Deforestation in the Amazon rainforest is an alarming problem of global interest. Environmental impacts of this process are countless, but probably the most significant concerns regard the increase in CO2 emissions and global temperature rise. Currently, the assessment of deforested areas in the Amazon region is a manual task, where people analyse multiple satellite images to quantify the deforestation. We propose a method for automatic deforestation detection based on Deep Learning Neural Networks with dual-attention mechanisms. We employed a siamese architecture to detect deforestation changes between optical images in 2018 and 2019. Experiments were performed to evaluate the relevance and sensitivity of hyperparameter tuning of the loss function and the effects of dual-attention mechanisms (spatial and channel) in predicting deforestation. Experimental results suggest that a proper tuning of the loss function might bring benefits in terms of generalisation. We also show that the spatial attention mechanism is more relevant for deforestation detection than the channel attention mechanism. When both mechanisms are combined, the greatest improvements are found, and we reported an increase of 1.06% in the mean average precision over a baseline.

scholarly journals Amazon Rainforest Deforestation Daily Detection Tool Using Artificial Neural Networks and Satellite Images

2012 ◽  
Vol 4 (10) ◽  
pp. 2566-2573 ◽  
Author(s):  
Thiago Nunes Kehl ◽  
Viviane Todt ◽  
Mauricio Roberto Veronez ◽  
Silvio Cazella
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Satellite Images ◽  
Amazon Rainforest ◽  
Detection Tool ◽  
Artificial Neural

scholarly journals Uncertainty-Based Human-in-the-Loop Deep Learning for Land Cover Segmentation

2020 ◽  
Vol 12 (22) ◽  
pp. 3836
Author(s):  
Carlos García Rodríguez ◽  
Jordi Vitrià ◽  
Oscar Mora
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Land Cover ◽  
Satellite Images ◽  
State Of The Art ◽  
Low Cost ◽  
Human Intervention ◽  
Human In The Loop ◽  
Learning Techniques

In recent years, different deep learning techniques were applied to segment aerial and satellite images. Nevertheless, state of the art techniques for land cover segmentation does not provide accurate results to be used in real applications. This is a problem faced by institutions and companies that want to replace time-consuming and exhausting human work with AI technology. In this work, we propose a method that combines deep learning with a human-in-the-loop strategy to achieve expert-level results at a low cost. We use a neural network to segment the images. In parallel, another network is used to measure uncertainty for predicted pixels. Finally, we combine these neural networks with a human-in-the-loop approach to produce correct predictions as if developed by human photointerpreters. Applying this methodology shows that we can increase the accuracy of land cover segmentation tasks while decreasing human intervention.

scholarly journals Investigation of optimal configurations of a convolutional neural network for the identification of objects in real-time

2019 ◽  
pp. 417-423
Author(s):  
M A Isayev ◽  
D A Savelyev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real Time ◽  
State Of The Art ◽  
Average Precision ◽  
The Core ◽  
Particular Solution ◽  
Optimal Configurations

The comparison of different convolutional neural networks which are the core of the most actual solutions in the computer vision area is considers in hhe paper. The study includes benchmarks of this state-of-the-art solutions by some criteria, such as mAP (mean average precision), FPS (frames per seconds), for the possibility of real-time usability. It is concluded on the best convolutional neural network model and deep learning methods that were used at particular solution.

scholarly journals Interpretable attention model in transcription factor binding site prediction with deep neural networks

2019 ◽  
Author(s):  
Chen Chen ◽  
Jie Hou ◽  
Xiaowen Shi ◽  
Hua Yang ◽  
James A. Birchler ◽  
...  
Keyword(s):  
Neural Networks ◽  
Transcription Factor ◽  
Deep Learning ◽  
Binding Site ◽  
Transcription Factor Binding Site ◽  
Attention Mechanism ◽  
Factor Binding Site ◽  
Site Prediction ◽  
Factor Binding ◽  
Binding Site Prediction

AbstractDue to the complexity of the biological factors that may influence the binding of transcription factors to DNA sequences, prediction of the potential binding sites remains a difficult task in computational biology. The attention mechanism in deep learning has shown its capability to learn from input features with long-range dependencies. Until now, no study has applied this mechanism in deep neural network models with input data from massively parallel sequencing. In this study, we aim to build a model for binding site prediction with the combination of attention mechanism and traditional deep learning techniques, including convolutional neural networks and recurrent neural networks. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets.The benchmark shows that our implementation with attention mechanism (called DeepGRN) improves the performance of the deep learning models. Our model achieves better performance in at least 9 of 13 targets than any of the methods participated in the DREAM challenge. Visualization of the attention weights extracted from the trained models reveals how those weights shift when binding signal peaks move along the genomic sequence, which can interpret how the predictions are made. Case studies show that the attention mechanism helps to extract useful features by focusing on regions that are critical to successful prediction while ignoring the irrelevant signals from the input.

Automatic seismic facies interpretation using supervised deep learning

Geophysics ◽  
2020 ◽  
pp. 1-113
Author(s):  
Haoran Zhang ◽  
Tiansheng Chen ◽  
Yang Liu ◽  
Yuxi Zhang ◽  
Jiong Liu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
The Netherlands ◽  
Seismic Facies ◽  
Seismic Section ◽  
Tuning Parameters ◽  
Data Volume ◽  
The Mean ◽  
Seismic Sections ◽  
Geological Background

Seismic facies interpretation supports subsurface geologic environment analyses and reservoir predictions. Traditional interpretation methods require much manual work, and heavily depend on experience and expertise of interpreters. Now we adopt advanced algorithms from supervised deep learning to perform automatic seismic facies interpretations. In deep learning, conventional convolutional neural networks (CNNs) and encoder-decoder architectures are widely used for image classification and segmentation problems, respectively. Based on these two architectures, we build a 3D conventional CNN and a conventional encoder-decoder, then apply an enhanced encoder-decoder (Deeplabv3+) that integrates superior structures to our research. To train the networks, we propose an effective scheme to automatically and diversely augment data by using well labeled 2D seismic sections in which facies are divided into nine classes. We perform experiments on the Netherlands F3 dataset by training diverse samples and tuning parameters, and implement the trained networks on the whole data volume, then quantitatively evaluate the results. The testing results of the encoder-decoders are more accurate and efficient than those of the conventional CNN, as well as more consistent with the geological background. The mean intersection-over-union (mIoU) values for the encoder-decoders are 87.8% (conventional) and 92.4% (enhanced) respectively, while for the conventional CNN the value is 67.8%. Besides, the prediction of one seismic section takes less than 1.0 second for the encoder-decoders, whereas it takes 4.0 minutes for the conventional CNN.

scholarly journals Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2167 ◽  
Author(s):  
Nicolas Aguirre ◽  
Edith Grall-Maës ◽  
Leandro J. Cymberknop ◽  
Ricardo L. Armentano
Keyword(s):  
Blood Pressure ◽  
Deep Learning ◽  
Attention Mechanism ◽  
Wireless Devices ◽  
Demographic Information ◽  
Use Of Data ◽  
Arterial Blood ◽  
The Mean ◽  
And Gender ◽  
Deep Learning Model

Arterial blood pressure (ABP) is an important vital sign from which it can be extracted valuable information about the subject’s health. After studying its morphology it is possible to diagnose cardiovascular diseases such as hypertension, so ABP routine control is recommended. The most common method of controlling ABP is the cuff-based method, from which it is obtained only the systolic and diastolic blood pressure (SBP and DBP, respectively). This paper proposes a cuff-free method to estimate the morphology of the average ABP pulse (ABPM¯) through a deep learning model based on a seq2seq architecture with attention mechanism. It only needs raw photoplethysmogram signals (PPG) from the finger and includes the capacity to integrate both categorical and continuous demographic information (DI). The experiments were performed on more than 1100 subjects from the MIMIC database for which their corresponding age and gender were consulted. Without allowing the use of data from the same subjects to train and test, the mean absolute errors (MAE) were 6.57 ± 0.20 and 14.39 ± 0.42 mmHg for DBP and SBP, respectively. For ABPM¯, R correlation coefficient and the MAE were 0.98 ± 0.001 and 8.89 ± 0.10 mmHg. In summary, this methodology is capable of transforming PPG into an ABP pulse, which obtains better results when DI of the subjects is used, potentially useful in times when wireless devices are becoming more popular.

scholarly journals Mice Tracking Using The YOLO Algorithm

2020 ◽  
Author(s):  
Richardson Santiago Teles De Menezes ◽  
John Victor Alves Luiz ◽  
Aron Miranda Henrique-Alves ◽  
Rossana Moreno Santa Cruz ◽  
Helton Maia
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Behavioral Neuroscience ◽  
High Accuracy ◽  
Mean Average Precision ◽  
Computational Tool ◽  
Training Set ◽  
Average Precision ◽  
The Mean

The computational tool developed in this study is based on convolutional neural networks and the You Only Look Once (YOLO) algorithm for detecting and tracking mice in videos recorded during behavioral neuroscience experiments. We analyzed a set of data composed of 13622 images, made up of behavioral videos of three important researches in this area. The training set used 50% of the images, 25% for validation, and 25% for the tests. The results show that the mean Average Precision (mAP) reached by the developed system was 90.79% and 90.75% for the Full and Tiny versions of YOLO, respectively. Considering the high accuracy of the results, the developed work allows the experimentalists to perform mice tracking in a reliable and non-evasive way.

scholarly journals Confidence-Aware Object Detection Based on MobileNetv2 for Autonomous Driving

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2380
Author(s):  
Wei Li ◽  
Kai Liu
Keyword(s):  
Object Detection ◽  
Loss Function ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Average Precision ◽  
Detection Model ◽  
Multi Scale ◽  
Proposed Model ◽  
The Mean ◽  
High Level

Object detection is an indispensable part of autonomous driving. It is the basis of other high-level applications. For example, autonomous vehicles need to use the object detection results to navigate and avoid obstacles. In this paper, we propose a multi-scale MobileNeck module and an algorithm to improve the performance of an object detection model by outputting a series of Gaussian parameters. These Gaussian parameters can be used to predict both the locations of detected objects and the localization confidences. Based on the above two methods, a new confidence-aware Mobile Detection (MobileDet) model is proposed. The MobileNeck module and loss function are easy to conduct and integrate with Generalized-IoU (GIoU) metrics with slight changes in the code. We test the proposed model on the KITTI and VOC datasets. The mean Average Precision (mAP) is improved by 3.8 on the KITTI dataset and 2.9 on the VOC dataset with less resource consumption.

scholarly journals Deep learning-based detection of dental prostheses and restorations

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Toshihito Takahashi ◽  
Kazunori Nozaki ◽  
Tomoya Gonda ◽  
Tomoaki Mameno ◽  
Kazunori Ikebe
Keyword(s):  
Deep Learning ◽  
High Accuracy ◽  
Mean Average Precision ◽  
Learning Performance ◽  
Average Precision ◽  
Tooth Color ◽  
Photographic Images ◽  
Dental Prostheses ◽  
Learning Procedure ◽  
The Mean

AbstractThe purpose of this study is to develop a method for recognizing dental prostheses and restorations of teeth using a deep learning. A dataset of 1904 oral photographic images of dental arches (maxilla: 1084 images; mandible: 820 images) was used in the study. A deep-learning method to recognize the 11 types of dental prostheses and restorations was developed using TensorFlow and Keras deep learning libraries. After completion of the learning procedure, the average precision of each prosthesis, mean average precision, and mean intersection over union were used to evaluate learning performance. The average precision of each prosthesis varies from 0.59 to 0.93. The mean average precision and mean intersection over union of this system were 0.80 and 0.76, respectively. More than 80% of metallic dental prostheses were detected correctly, but only 60% of tooth-colored prostheses were detected. The results of this study suggest that dental prostheses and restorations that are metallic in color can be recognized and predicted with high accuracy using deep learning; however, those with tooth color are recognized with moderate accuracy.

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