scholarly journals Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN

2020 ◽  
Vol 12 (2) ◽  
pp. 338 ◽  
Author(s):  
Yunling Liu ◽  
Chaojun Cen ◽  
Yingpu Che ◽  
Rui Ke ◽  
Yan Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Mobile Phone ◽  
Prediction Accuracy ◽  
Critical Role ◽  
Growth And Yield ◽  
Detection Accuracy ◽  
Branch Number ◽  
Aerial Vehicle ◽  
Length Width ◽  
Uav Images

Maize tassels play a critical role in plant growth and yield. Extensive RGB images obtained using unmanned aerial vehicle (UAV) and the prevalence of deep learning provide a chance to improve the accuracy of detecting maize tassels. We used images from UAV, a mobile phone, and the Maize Tassel Counting dataset (MTC) to test the performance of faster region-based convolutional neural network (Faster R-CNN) with residual neural network (ResNet) and a visual geometry group neural network (VGGNet). The results showed that the ResNet, as the feature extraction network, was better than the VGGNet for detecting maize tassels from UAV images with 600 × 600 resolution. The prediction accuracy ranged from 87.94% to 94.99%. However, the prediction accuracy was less than 87.27% from the UAV images with 5280 × 2970 resolution. We modified the anchor size to [852, 1282, 2562] in the region proposal network according to the width and height of pixel distribution to improve detection accuracy up to 89.96%. The accuracy reached up to 95.95% for mobile phone images. Then, we compared our trained model with TasselNet without training their datasets. The average difference of tassel number was 1.4 between the calculations with 40 images for the two methods. In the future, we could further improve the performance of the models by enlarging datasets and calculating other tassel traits such as the length, width, diameter, perimeter, and the branch number of the maize tassels.

scholarly journals High-Resolution Neural Network for Driver Visual Attention Prediction

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2030 ◽  
Author(s):  
Byeongkeun Kang ◽  
Yeejin Lee
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Visual Attention ◽  
Visual Information ◽  
Prediction Accuracy ◽  
Critical Role ◽  
Image Resolution ◽  
Feature Representation ◽  
Safe Driving ◽  
Feature Representations

Driving is a task that puts heavy demands on visual information, thereby the human visual system plays a critical role in making proper decisions for safe driving. Understanding a driver’s visual attention and relevant behavior information is a challenging but essential task in advanced driver-assistance systems (ADAS) and efficient autonomous vehicles (AV). Specifically, robust prediction of a driver’s attention from images could be a crucial key to assist intelligent vehicle systems where a self-driving car is required to move safely interacting with the surrounding environment. Thus, in this paper, we investigate a human driver’s visual behavior in terms of computer vision to estimate the driver’s attention locations in images. First, we show that feature representations at high resolution improves visual attention prediction accuracy and localization performance when being fused with features at low-resolution. To demonstrate this, we employ a deep convolutional neural network framework that learns and extracts feature representations at multiple resolutions. In particular, the network maintains the feature representation with the highest resolution at the original image resolution. Second, attention prediction tends to be biased toward centers of images when neural networks are trained using typical visual attention datasets. To avoid overfitting to the center-biased solution, the network is trained using diverse regions of images. Finally, the experimental results verify that our proposed framework improves the prediction accuracy of a driver’s attention locations.

scholarly journals Neural Networks for Infectious Diseases Detection: Prospects and Challenges

2021 ◽  
Author(s):  
Shumaila Javaid ◽  
Nasir Saeed
Keyword(s):  
Neural Network ◽  
Data Privacy ◽  
Critical Role ◽  
Disease Diagnosis ◽  
Future Research ◽  
Detection Accuracy ◽  
Privacy And Security ◽  
Research Directions ◽  
Medical Diagnostic ◽  
Artificial Neural Network Ann

Artificial neural network (ANN) ability to learn, correct errors, and transform a large amount of raw data into useful medical decisions for treatment and care have increased its popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients’ healthcare decisions and efficient disease diagnosis. We thoroughly review different types of ANNs presented in the existing literature that advanced ANNs adaptation for complex applications. Moreover, we also investigate ANN’s advances for various disease diagnoses and treatments such as viral, skin, cancer, and COVID-19. Furthermore, we propose a novel deep Convolutional Neural Network (CNN) model called ConXNet for improving the detection accuracy of COVID-19 disease. ConXNet is trained and tested using different datasets, and it achieves more than 97% detection accuracy and precision, which is significantly better than existing models. Finally, we highlight future research directions and challenges such as complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications. <br>

scholarly journals Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection

2021 ◽  
Vol 13 (21) ◽  
pp. 4377
Author(s):  
Long Sun ◽  
Jie Chen ◽  
Dazheng Feng ◽  
Mengdao Xing
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Target Detection ◽  
Template Matching ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Information Warfare ◽  
Aerial Vehicle ◽  
Uav Images

Unmanned aerial vehicle (UAV) is one of the main means of information warfare, such as in battlefield cruises, reconnaissance, and military strikes. Rapid detection and accurate recognition of key targets in UAV images are the basis of subsequent military tasks. The UAV image has characteristics of high resolution and small target size, and in practical application, the detection speed is often required to be fast. Existing algorithms are not able to achieve an effective trade-off between detection accuracy and speed. Therefore, this paper proposes a parallel ensemble deep learning framework for unmanned aerial vehicle video multi-target detection, which is a global and local joint detection strategy. It combines a deep learning target detection algorithm with template matching to make full use of image information. It also integrates multi-process and multi-threading mechanisms to speed up processing. Experiments show that the system has high detection accuracy for targets with focal lengths varying from one to ten times. At the same time, the real-time and stable display of detection results is realized by aiming at the moving UAV video image.

scholarly journals Detection of a Moving UAV Based on Deep Learning-Based Distance Estimation

2020 ◽  
Vol 12 (18) ◽  
pp. 3035
Author(s):  
Ying-Chih Lai ◽  
Zong-Ying Huang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Moving Objects ◽  
Distance Estimation ◽  
Active Vision ◽  
Detection Accuracy ◽  
Distance Information ◽  
Sense And Avoid ◽  
Object Distance ◽  
Aerial Vehicle

Distance information of an obstacle is important for obstacle avoidance in many applications, and could be used to determine the potential risk of object collision. In this study, the detection of a moving fixed-wing unmanned aerial vehicle (UAV) with deep learning-based distance estimation to conduct a feasibility study of sense and avoid (SAA) and mid-air collision avoidance of UAVs is proposed by using a monocular camera to detect and track an incoming UAV. A quadrotor is regarded as an owned UAV, and it is able to estimate the distance of an incoming fixed-wing intruder. The adopted object detection method is based on the you only look once (YOLO) object detector. Deep neural network (DNN) and convolutional neural network (CNN) methods are applied to exam their performance in the distance estimation of moving objects. The feature extraction of fixed-wing UAVs is based on the VGG-16 model, and then its result is applied to the distance network to estimate the object distance. The proposed model is trained by using synthetic images from animation software and validated by using both synthetic and real flight videos. The results show that the proposed active vision-based scheme is able to detect and track a moving UAV with high detection accuracy and low distance errors.

scholarly journals Maize Tassel Detection From UAV Imagery Using Deep Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Aziza Alzadjali ◽  
Mohammed H. Alali ◽  
Arun Narenthiran Veeranampalayam Sivakumar ◽  
Jitender S. Deogun ◽  
Stephen Scott ◽  
...  
Keyword(s):  
Deep Learning ◽  
Critical Role ◽  
Evaluation Criteria ◽  
Detection Accuracy ◽  
Art Object ◽  
Aerial Vehicle ◽  
Improved Accuracy ◽  
Baseline Detection ◽  
Model Structures ◽  
Agricultural Setting

The timing of flowering plays a critical role in determining the productivity of agricultural crops. If the crops flower too early, the crop would mature before the end of the growing season, losing the opportunity to capture and use large amounts of light energy. If the crops flower too late, the crop may be killed by the change of seasons before it is ready to harvest. Maize flowering is one of the most important periods where even small amounts of stress can significantly alter yield. In this work, we developed and compared two methods for automatic tassel detection based on the imagery collected from an unmanned aerial vehicle, using deep learning models. The first approach was a customized framework for tassel detection based on convolutional neural network (TD-CNN). The other method was a state-of-the-art object detection technique of the faster region-based CNN (Faster R-CNN), serving as baseline detection accuracy. The evaluation criteria for tassel detection were customized to correctly reflect the needs of tassel detection in an agricultural setting. Although detecting thin tassels in the aerial imagery is challenging, our results showed promising accuracy: the TD-CNN had an F1 score of 95.9% and the Faster R-CNN had 97.9% F1 score. More CNN-based model structures can be investigated in the future for improved accuracy, speed, and generalizability on aerial-based tassel detection.

scholarly journals Application of a Convolutional Neural Network for the Detection of Sea Ice Leads

2021 ◽  
Vol 13 (22) ◽  
pp. 4571
Author(s):  
Jay P. Hoffman ◽  
Steven A. Ackerman ◽  
Yinghui Liu ◽  
Jeffrey R. Key ◽  
Iain L. McConnell
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Convolutional Neural Network ◽  
Infrared Imaging ◽  
Critical Role ◽  
The Arctic ◽  
Detection Accuracy ◽  
New Approach ◽  
Novel Approach ◽  
Ice Leads

Despite accounting for a small fraction of the surface area in the Arctic, long and narrow sea ice fractures, known as “leads”, play a critical role in the energy flux between the ocean and atmosphere. As the volume of sea ice in the Arctic has declined over the past few decades, it is increasingly important to monitor the corresponding changes in sea ice leads. A novel approach has been developed using artificial intelligence (AI) to detect sea ice leads using satellite thermal infrared window data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS). In this new approach, a particular type of convolutional neural network, a U-Net, replaces a series of conventional image processing tests from our legacy algorithm. Results show the new approach has a high detection accuracy with F1 Scores on the order of 0.7. Compared to the legacy algorithm, the new algorithm shows improvement, with more true positives, fewer false positives, fewer false negatives, and better agreement between satellite instruments.

ProEmotion: A Tool to Tell Mobile Phone’s Gender

2010 ◽  
Author(s):  
William W. Wang ◽  
Xun W. Xu ◽  
Hsiang-Hung Hsiao
Keyword(s):  
Neural Network ◽  
Mobile Phone ◽  
Product Design ◽  
Kansei Engineering ◽  
Design Attributes ◽  
Product Function ◽  
Length Width

Kansei engineering, also known as kansei ergonomics or emotional engineering, aims at analyzing and incorporating customer’s feeling and demands into product function and product design. The paper described a system called ProEmotion for the purpose of assessing the kansei aspects of a product by considering design attributes of a product. Neural network is used to process kansei words. The system has been successfully implemented to ascertain gender inclination of a mobile phone. Principal parameters of a mobile are considered, i.e. length, width, thickness and mass. The system can inform gender inclination of a mobile phone with accuracy up to 90%. This is based on a set of 92 mobile phone samples from the five major mobile phone manufacturers.

scholarly journals Neural Networks for Infectious Diseases Detection: Prospects and Challenges

2021 ◽  
Author(s):  
Shumaila Javaid ◽  
Nasir Saeed
Keyword(s):  
Neural Network ◽  
Data Privacy ◽  
Critical Role ◽  
Disease Diagnosis ◽  
Future Research ◽  
Detection Accuracy ◽  
Privacy And Security ◽  
Research Directions ◽  
Medical Diagnostic ◽  
Artificial Neural Network Ann

Artificial neural network (ANN) ability to learn, correct errors, and transform a large amount of raw data into useful medical decisions for treatment and care have increased its popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients’ healthcare decisions and efficient disease diagnosis. We thoroughly review different types of ANNs presented in the existing literature that advanced ANNs adaptation for complex applications. Moreover, we also investigate ANN’s advances for various disease diagnoses and treatments such as viral, skin, cancer, and COVID-19. Furthermore, we propose a novel deep Convolutional Neural Network (CNN) model called ConXNet for improving the detection accuracy of COVID-19 disease. ConXNet is trained and tested using different datasets, and it achieves more than 97% detection accuracy and precision, which is significantly better than existing models. Finally, we highlight future research directions and challenges such as complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications. <br>

scholarly journals Anomaly detection and missing data imputation in building energy data for automated data pre-processing

2021 ◽  
Vol 2069 (1) ◽  
pp. 012144
Author(s):  
K Takahashi ◽  
R Ooka ◽  
S Ikeda
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Missing Data ◽  
Anomaly Detection ◽  
Prediction Accuracy ◽  
Energy Savings ◽  
Predictive Accuracy ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Detection Accuracy

Abstract A new trend in building automation is the implementation of smart energy management systems to measure and control building systems without a need for decision-making by human operators. Artificial intelligence can optimize these systems by predicting future demand to make informed decisions about how to efficiently operate individual equipment. These machine learning algorithms use historical data to learn demand trends and require high quality datasets in order to make accurate predictions. But because of issues with data transmission or sensor errors, real world datasets often contain outliers or have data missing. In most research settings, these values can be simply omitted, but in practice, anomalies compromise the automation system’s prediction accuracy, rendering it unable to maximize energy savings. This study explores different machine learning algorithms for anomaly detection for automatically pre-processing incoming data using a case study on an actual electrical demand in a hospital building in Japan, namely cluster-based techniques such as k-means clustering and neural network-based approaches such as the autoencoder. Once anomalies were identified, the missing data was filled with prediction values from a deep neural network model. The newly composed data was then evaluated based on detection accuracy, prediction accuracy and training time. The proposed method of processing anomaly values allows the prediction model to process collected data without interruption, and shows similar predictive accuracy as manually processing the data. These predictions allow energy systems to optimize HVAC equipment control, increasing energy savings and reducing peak building loads.

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