Deep-Learning-Based Automated Palm Tree Counting and Geolocation in Large Farms from Aerial Geotagged Images

In this paper, we propose an original deep learning framework for the automated counting and geolocation of palm trees from aerial images using convolutional neural networks. For this purpose, we collected aerial images from two different regions in Saudi Arabia, using two DJI drones, and we built a dataset of around 11,000 instances of palm trees. Then, we applied several recent convolutional neural network models (Faster R-CNN, YOLOv3, YOLOv4, and EfficientDet) to detect palms and other trees, and we conducted a complete comparative evaluation in terms of average precision and inference speed. YOLOv4 and EfficientDet-D5 yielded the best trade-off between accuracy and speed (up to 99% mean average precision and 7.4 FPS). Furthermore, using the geotagged metadata of aerial images, we used photogrammetry concepts and distance corrections to automatically detect the geographical location of detected palm trees. This geolocation technique was tested on two different types of drones (DJI Mavic Pro and Phantom 4 pro) and was assessed to provide an average geolocation accuracy that attains 1.6 m. This GPS tagging allows us to uniquely identify palm trees and count their number from a series of drone images, while correctly dealing with the issue of image overlapping. Moreover, this innovative combination between deep learning object detection and geolocalization can be generalized to any other objects in UAV images.

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Deep learning and complex network theory based analysis on socialized manufacturing resources utilisations and an application case study

Concurrent Engineering ◽

10.1177/1063293x211003194 ◽

2021 ◽

pp. 1063293X2110031

Author(s):

Maolin Yang ◽

Auwal H Abubakar ◽

Pingyu Jiang

Keyword(s):

Deep Learning ◽

Complex Network ◽

Network Models ◽

Neural Network Models ◽

Complex Network Theory ◽

New Type ◽

Manufacturing Resource ◽

Manufacturing Resources ◽

Resource Characteristics

Social manufacturing is characterized by its capability of utilizing socialized manufacturing resources to achieve value adding. Recently, a new type of social manufacturing pattern emerges and shows potential for core factories to improve their limited manufacturing capabilities by utilizing the resources from outside socialized manufacturing resource communities. However, the core factories need to analyze the resource characteristics of the socialized resource communities before making operation plans, and this is challenging due to the unaffiliated and self-driven characteristics of the resource providers in socialized resource communities. In this paper, a deep learning and complex network based approach is established to address this challenge by using socialized designer community for demonstration. Firstly, convolutional neural network models are trained to identify the design resource characteristics of each socialized designer in designer community according to the interaction texts posted by the socialized designer on internet platforms. During the process, an iterative dataset labelling method is established to reduce the time cost for training set labelling. Secondly, complex networks are used to model the design resource characteristics of the community according to the resource characteristics of all the socialized designers in the community. Two real communities from RepRap 3D printer project are used as case study.

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Feature extraction-based image steganalysis using deep learning

WEENTECH Proceedings in Energy ◽

10.32438/wpe.182021 ◽

2021 ◽

pp. 188-198

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Secure Communication ◽

Information Technologies ◽

Network Models ◽

Error Rates ◽

Multimedia Data ◽

Secret Message ◽

Neural Network Models

The innovations in advanced information technologies has led to rapid delivery and sharing of multimedia data like images and videos. The digital steganography offers ability to secure communication and imperative for internet. The image steganography is essential to preserve confidential information of security applications. The secret image is embedded within pixels. The embedding of secret message is done by applied with S-UNIWARD and WOW steganography. Hidden messages are reveled using steganalysis. The exploration of research interests focused on conventional fields and recent technological fields of steganalysis. This paper devises Convolutional neural network models for steganalysis. Convolutional neural network (CNN) is one of the most frequently used deep learning techniques. The Convolutional neural network is used to extract spatio-temporal information or features and classification. We have compared steganalysis outcome with AlexNet and SRNeT with same dataset. The stegnalytic error rates are compared with different payloads.

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DEEP LEARNING FOR MONOCULAR DEPTH ESTIMATION FROM UAV IMAGES

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-2-2020-451-2020 ◽

2020 ◽

Vol V-2-2020 ◽

pp. 451-458

Author(s):

L. Madhuanand ◽

F. Nex ◽

M. Y. Yang

Keyword(s):

Deep Learning ◽

Ground Level ◽

Depth Estimation ◽

Aerial Images ◽

Aerial Image ◽

Depth Information ◽

Single Image ◽

Monocular Depth ◽

Uav Images ◽

Image Depth

Abstract. Depth is an essential component for various scene understanding tasks and for reconstructing the 3D geometry of the scene. Estimating depth from stereo images requires multiple views of the same scene to be captured which is often not possible when exploring new environments with a UAV. To overcome this monocular depth estimation has been a topic of interest with the recent advancements in computer vision and deep learning techniques. This research has been widely focused on indoor scenes or outdoor scenes captured at ground level. Single image depth estimation from aerial images has been limited due to additional complexities arising from increased camera distance, wider area coverage with lots of occlusions. A new aerial image dataset is prepared specifically for this purpose combining Unmanned Aerial Vehicles (UAV) images covering different regions, features and point of views. The single image depth estimation is based on image reconstruction techniques which uses stereo images for learning to estimate depth from single images. Among the various available models for ground-level single image depth estimation, two models, 1) a Convolutional Neural Network (CNN) and 2) a Generative Adversarial model (GAN) are used to learn depth from aerial images from UAVs. These models generate pixel-wise disparity images which could be converted into depth information. The generated disparity maps from these models are evaluated for its internal quality using various error metrics. The results show higher disparity ranges with smoother images generated by CNN model and sharper images with lesser disparity range generated by GAN model. The produced disparity images are converted to depth information and compared with point clouds obtained using Pix4D. It is found that the CNN model performs better than GAN and produces depth similar to that of Pix4D. This comparison helps in streamlining the efforts to produce depth from a single aerial image.

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Deep Network Guided Proof Search

10.29007/8mwc ◽

2018 ◽

Cited By ~ 1

Author(s):

Sarah Loos ◽

Geoffrey Irving ◽

Christian Szegedy ◽

Cezary Kaliszyk

Keyword(s):

Deep Learning ◽

Program Analysis ◽

Network Models ◽

Theorem Prover ◽

Neural Network Models ◽

Two Phase ◽

First Order ◽

Proof Search ◽

System Verification ◽

Theory Reasoning

Deep learning techniques lie at the heart of several significant AI advances in recent years including object recognition and detection, image captioning, machine translation, speech recognition and synthesis, and playing the game of Go.Automated first-order theorem provers can aid in the formalization and verification of mathematical theorems and play a crucial role in program analysis, theory reasoning, security, interpolation, and system verification.Here we suggest deep learning based guidance in the proof search of the theorem prover E. We train and compare several deep neural network models on the traces of existing ATP proofs of Mizar statements and use them to select processed clauses during proof search. We give experimental evidence that with a hybrid, two-phase approach, deep learning based guidance can significantly reduce the average number of proof search steps while increasing the number of theorems proved.Using a few proof guidance strategies that leverage deep neural networks, we have found first-order proofs of 7.36% of the first-order logic translations of the Mizar Mathematical Library theorems that did not previously have ATP generated proofs. This increases the ratio of statements in the corpus with ATP generated proofs from 56% to 59%.

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SEALing Neural Network Models in Encrypted Deep Learning Accelerators

10.1109/dac18074.2021.9586199 ◽

2021 ◽

Author(s):

Pengfei Zuo ◽

Yu Hua ◽

Ling Liang ◽

Xinfeng Xie ◽

Xing Hu ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Network Models ◽

Neural Network Models

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Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps

Machine Learning and Knowledge Extraction ◽

10.3390/make1010027 ◽

2019 ◽

Vol 1 (1) ◽

pp. 450-465 ◽

Cited By ~ 8

Author(s):

Abhishek Sehgal ◽

Nasser Kehtarnavaz

Keyword(s):

Deep Learning ◽

Real Time ◽

Open Source Software ◽

Mobile Applications ◽

Network Models ◽

Software Tools ◽

Learning Tools ◽

Smartphone Apps ◽

Learning Models ◽

Neural Network Models

Deep learning solutions are being increasingly used in mobile applications. Although there are many open-source software tools for the development of deep learning solutions, there are no guidelines in one place in a unified manner for using these tools toward real-time deployment of these solutions on smartphones. From the variety of available deep learning tools, the most suited ones are used in this paper to enable real-time deployment of deep learning inference networks on smartphones. A uniform flow of implementation is devised for both Android and iOS smartphones. The advantage of using multi-threading to achieve or improve real-time throughputs is also showcased. A benchmarking framework consisting of accuracy, CPU/GPU consumption, and real-time throughput is considered for validation purposes. The developed deployment approach allows deep learning models to be turned into real-time smartphone apps with ease based on publicly available deep learning and smartphone software tools. This approach is applied to six popular or representative convolutional neural network models, and the validation results based on the benchmarking metrics are reported.

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Beluga whale acoustic signal classification using deep learning neural network models

The Journal of the Acoustical Society of America ◽

10.1121/10.0000921 ◽

2020 ◽

Vol 147 (3) ◽

pp. 1834-1841 ◽

Cited By ~ 1

Author(s):

Ming Zhong ◽

Manuel Castellote ◽

Rahul Dodhia ◽

Juan Lavista Ferres ◽

Mandy Keogh ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Acoustic Signal ◽

Network Models ◽

Beluga Whale ◽

Signal Classification ◽

Neural Network Models ◽

Deep Learning Neural Network

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Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Information ◽

10.3390/info11040207 ◽

2020 ◽

Vol 11 (4) ◽

pp. 207

Author(s):

Asma Baccouche ◽

Begonya Garcia-Zapirain ◽

Cristian Castillo Olea ◽

Adel Elmaghraby

Keyword(s):

Neural Network ◽

Heart Disease ◽

Ensemble Learning ◽

Heart Diseases ◽

Hypertensive Heart Disease ◽

Network Models ◽

Features Selection ◽

Neural Network Models ◽

Learning Framework ◽

Different Types

Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.

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Application of Deep Learning for Characterization of Drivers’ Engagement in Secondary Tasks in In-Vehicle Systems

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120926507 ◽

2020 ◽

Vol 2674 (8) ◽

pp. 429-440

Author(s):

Osama A. Osman ◽

Hesham Rakha

Keyword(s):

Neural Network ◽

Deep Learning ◽

Time Series Data ◽

Short Term Memory ◽

Network Models ◽

Sensor Technology ◽

Series Data ◽

Neural Network Models ◽

Secondary Tasks ◽

Driving Assistance

Distracted driving (i.e., engaging in secondary tasks) is an epidemic that threatens the lives of thousands every year. Data collected from vehicular sensor technologies and through connectivity provide comprehensive information that, if used to detect driver engagement in secondary tasks, could save thousands of lives and millions of dollars. This study investigates the possibility of achieving this goal using promising deep learning tools. Specifically, two deep neural network models (a multilayer perceptron neural network model and a long short-term memory networks [LSTMN] model) were developed to identify three secondary tasks: cellphone calling, cellphone texting, and conversation with adjacent passengers. The Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) time series data, collected using vehicle sensor technology, were used to train and test the model. The results show excellent performance for the developed models, with a slight improvement for the LSTMN model, with overall classification accuracies ranging between 95 and 96%. Specifically, the models are able to identify the different types of secondary tasks with high accuracies of 100% for calling, 96%–97% for texting, 90%–91% for conversation, and 95%–96% for the normal driving. Based on this performance, the developed models improve on the results of a previous model developed by the author to classify the same three secondary tasks, which had an accuracy of 82%. The model is promising for use in in-vehicle driving assistance technology to report engagement in unlawful tasks or alert drivers to take over control in level 1 and 2 automated vehicles.

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