Operation Skill Acquisition and Fuzzy-Rule Extraction for Drone Control Based on Visual Information Using Deep Learning

2020 ◽  
Vol 24 (3) ◽  
pp. 386-395
Author(s):  
Yoichiro Maeda ◽  
Kotaro Sano ◽  
Eric W. Cooper ◽  
Katsuari Kamei ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Skill Acquisition ◽  
Visual Information ◽  
Autonomous Vehicles ◽  
High Performance ◽  
Fuzzy Rule ◽  
Motor Vehicles ◽  
Moving Vehicle ◽  
Drone Control

In recent years, much research on the unmanned control of a moving vehicle has been conducted, and various robots and motor vehicles moving automatically are being used. However, the more complicated the environment is, the more difficult it is for the autonomous vehicles to move automatically. Even in such a challenging environment, however, an expert with the necessary operation skill can sometimes perform the appropriate control of the moving vehicle. In this research, a method for learning a human’s operation skill using a convolutional neural network (CNN) and setting visual information for input is proposed for learning more complicated environmental information. A CNN is a kind of deep-learning network, and it exhibits high performance in the field of image recognition. In this experiment, the operation knowledge was also visualized using a fuzzy neural network with obtained input-output maps to create fuzzy rules. To verify the effectiveness of this method, an experiment involving operation skill acquisition by some subjects using a drone control simulator was conducted.

scholarly journals Computer vision based obstacle detection and target tracking for autonomous vehicles

2021 ◽  
Vol 336 ◽  
pp. 07004
Author(s):  
Ruoyu Fang ◽  
Cheng Cai
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Target Tracking ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Obstacle Detection ◽  
Pid Algorithm ◽  
Deep Learning Neural Network

Obstacle detection and target tracking are two major issues for intelligent autonomous vehicles. This paper proposes a new scheme to achieve target tracking and real-time obstacle detection of obstacles based on computer vision. ResNet-18 deep learning neural network is utilized for obstacle detection and Yolo-v3 deep learning neural network is employed for real-time target tracking. These two trained models can be deployed on an autonomous vehicle equipped with an NVIDIA Jetson Nano motherboard. The autonomous vehicle moves to avoid obstacles and follow tracked targets by camera. Adjusting the steering and movement of the autonomous vehicle according to the PID algorithm during the movement, therefore, will help the proposed vehicle achieve stable and precise tracking.

Comprehensive Overview of Neural Networks and Its Applications in Autonomous Vehicles

2019 ◽  
pp. 159-173
Author(s):  
Jay Rodge ◽  
Swati Jaiswal
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Learning Algorithms ◽  
Activation Functions ◽  
Comprehensive Overview ◽  
Unit Component

Deep learning and Artificial intelligence (AI) have been trending these days due to the capability and state-of-the-art results that they provide. They have replaced some highly skilled professionals with neural network-powered AI, also known as deep learning algorithms. Deep learning majorly works on neural networks. This chapter discusses about the working of a neuron, which is a unit component of neural network. There are numerous techniques that can be incorporated while designing a neural network, such as activation functions, training, etc. to improve its features, which will be explained in detail. It has some challenges such as overfitting, which are difficult to neglect but can be overcome using proper techniques and steps that have been discussed. The chapter will help the academician, researchers, and practitioners to further investigate the associated area of deep learning and its applications in the autonomous vehicle industry.

scholarly journals Neural network activation similarity: a new measure to assist decision making in chemical toxicology

2020 ◽  
Vol 11 (28) ◽  
pp. 7335-7348 ◽  
Author(s):  
Timothy E. H. Allen ◽  
Andrew J. Wedlake ◽  
Elena Gelžinytė ◽  
Charles Gong ◽  
Jonathan M. Goodman ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Deep Learning ◽  
Test Data ◽  
High Performance ◽  
Chemical Binding ◽  
Biological Targets ◽  
Network Activation ◽  
Chemical Toxicology

Deep learning neural networks, constructed for the prediction of chemical binding at 79 pharmacologically important human biological targets, show extremely high performance on test data (accuracy 92.2 ± 4.2%, MCC 0.814 ± 0.093, ROC-AUC 0.96 ± 0.04).

Deep Learning-based Framework for Smart Sustainable Cities

2019 ◽  
Vol 15 (4) ◽  
pp. 76-107
Author(s):  
Nagarathna Ravi ◽  
Vimala Rani P ◽  
Rajesh Alias Harinarayan R ◽  
Mercy Shalinie S ◽  
Karthick Seshadri ◽  
...  
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Deep Learning ◽  
Air Pollutants ◽  
Human Life ◽  
Deep Belief Network ◽  
Motor Vehicles ◽  
Human Beings ◽  
Long Term Effects ◽  
Belief Network

Pure air is vital for sustaining human life. Air pollution causes long-term effects on people. There is an urgent need for protecting people from its profound effects. In general, people are unaware of the levels to which they are exposed to air pollutants. Vehicles, burning various kinds of waste, and industrial gases are the top three onset agents of air pollution. Of these three top agents, human beings are exposed frequently to the pollutants due to motor vehicles. To aid in protecting people from vehicular air pollutants, this article proposes a framework that utilizes deep learning models. The framework utilizes a deep belief network to predict the levels of air pollutants along the paths people travel and also a comparison with the predictions made by a feed forward neural network and an extreme learning machine. When evaluating the deep belief neural network for the case study undertaken, a deep belief network was able to achieve a higher index of agreement and lower RMSE values.

scholarly journals Optimizing Convolution Neural Network on the TI C6678 multicore DSP

2018 ◽  
Vol 246 ◽  
pp. 03044 ◽  
Author(s):  
Guozhao Zeng ◽  
Xiao Hu ◽  
Yueyue Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Digital Signal Processor ◽  
High Performance ◽  
Digital Signal ◽  
Automatic Translation ◽  
Convolution Operation ◽  
Multicore Dsp

Convolutional Neural Networks (CNNs) have become the most advanced algorithms for deep learning. They are widely used in image processing, object detection and automatic translation. As the demand for CNNs continues to increase, the platforms on which they are deployed continue to expand. As an excellent low-power, high-performance, embedded solution, Digital Signal Processor (DSP) is used frequently in many key areas. This paper attempts to deploy the CNN to Texas Instruments (TI)’s TMS320C6678 multi-core DSP and optimize the main operations (convolution) to accommodate the DSP structure. The efficiency of the improved convolution operation has increased by tens of times.

Low-observable targets detection for autonomous vehicles based on dual-modal sensor fusion with deep learning approach

2019 ◽  
Vol 233 (9) ◽  
pp. 2270-2283
Author(s):  
Keke Geng ◽  
Wei Zou ◽  
Guodong Yin ◽  
Yang Li ◽  
Zihao Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Deep Neural Network ◽  
Infrared Imaging ◽  
Infrared Image ◽  
Complex Environments ◽  
Infrared Images ◽  
Night Time ◽  
Perception System

Environment perception is a basic and necessary technology for autonomous vehicles to ensure safety and reliable driving. A lot of studies have focused on the ideal environment, while much less work has been done on the perception of low-observable targets, features of which may not be obvious in a complex environment. However, it is inevitable for autonomous vehicles to drive in environmental conditions such as rain, snow and night-time, during which the features of the targets are not obvious and detection models trained by images with significant features fail to detect low-observable target. This article mainly studies the efficient and intelligent recognition algorithm of low-observable targets in complex environments, focuses on the development of engineering method to dual-modal image (color–infrared images) low-observable target recognition and explores the applications of infrared imaging and color imaging for an intelligent perception system in autonomous vehicles. A dual-modal deep neural network is established to fuse the color and infrared images and detect low-observable targets in dual-modal images. A manually labeled color–infrared image dataset of low-observable targets is built. The deep learning neural network is trained to optimize internal parameters to make the system capable for both pedestrians and vehicle recognition in complex environments. The experimental results indicate that the dual-modal deep neural network has a better performance on the low-observable target detection and recognition in complex environments than traditional methods.

scholarly journals Convolutional Neural Network for Classification of Android Applications Represented as Grayscale Images

2019 ◽  
Vol 8 (12S) ◽  
pp. 835-843
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Performance ◽  
Smart Phone ◽  
Convolution Neural Network ◽  
Android Malware ◽  
Android Applications ◽  
Conventional Solution ◽  
Grayscale Images

A rapid dissemination of Android operating system in smart phone market has resulted in an exponential growth of threats to mobile applications. Various studies have been carried out in academia and industry for the identification and classification of malicious applications using machine learning and deep learning algorithms. Convolution Neural Network is a deep learning technique which has gained popularity in speech and image recognition. The conventional solution for identifying Android malware needs learning based on pre-extracted features to preserve high performance for detecting Android malware. In order to reduce the efforts and domain expertise involved in hand-feature engineering, we have generated the grayscale images of AndroidManifest.xml and classes.dex files which are extracted from the Android package and applied Convolution Neural Network for classifying the images. The experiments are conducted on a recent dataset of 1747 malicious Android applications. The results indicate that classes.dex file gives better results as compared to the AndroidManifest.xml and also demonstrate that model performs better as the image become larger.

Classification of EgyptSat-1 Images Using Deep Learning Methods

2020 ◽  
Vol 10 (1) ◽  
pp. 37-46 ◽  
Author(s):  
Hatem Keshk ◽  
Xu-Cheng Yin
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Performance ◽  
Satellite Images ◽  
Satellite Image ◽  
Aerial Photographs ◽  
Average Accuracy

Background: Deep Learning (DL) neural network methods have become a hotspot subject of research in the remote sensing field. Classification of aerial satellite images depends on spectral content, which is a challenging topic in remote sensing. Objective: With the aim to accomplish a high performance and accuracy of Egyptsat-1 satellite image classification, the use of the Convolutional Neural Network (CNN) is raised in this paper because CNN is considered a leading deep learning method. CNN is developed to classify aerial photographs into land cover classes such as urban, vegetation, desert, water bodies, soil, roads, etc. In our work, a comparison between MAXIMUM Likelihood (ML) which represents the traditional supervised classification methods and CNN method is conducted. Conclusion: This research finds that CNN outperforms ML by 9%. The convolutional neural network has better classification result, which reached 92.25% as its average accuracy. Also, the experiments showed that the convolutional neural network is the most satisfactory and effective classification method applied to classify Egyptsat-1 satellite images.

An End-to-End Fully Automatic Bay Parking Approach for Autonomous Vehicles

2018 ◽  
Author(s):  
Rui Li ◽  
Weitian Wang ◽  
Yi Chen ◽  
Srivatsan Srinivasan ◽  
Venkat N. Krovi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Human Vision ◽  
Automated Driving ◽  
Learning Framework ◽  
Research Platform ◽  
Automatic Parking ◽  
Fully Automatic

Fully automatic parking (FAP) is a key step towards the age of autonomous vehicle. Motivated by the contribution of human vision to human parking, in this paper, we propose a computer vision based FAP method for the autonomous vehicles. Based on the input images from a rear camera on the vehicle, a convolutional neural network (CNN) is trained to automatically output the steering and velocity commands for the vehicle controlling. The CNN is trained by Caffe deep learning framework. A 1/10th autonomous vehicle research platform (1/10-SAVRP), which configured with a vehicle controller unit, an automated driving processor, and a rear camera, is used for demonstrating the parking maneuver. The experimental results suggested that the proposed approach enabled the vehicle to gain the ability of parking independently without human input in different driving settings.

scholarly journals Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Na Li ◽  
Xinbo Zhao ◽  
Yongjia Yang ◽  
Xiaochun Zou
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Visual Attention ◽  
Convolutional Neural Network ◽  
Visual Information ◽  
Local Features ◽  
Classification Method ◽  
Semantic Features ◽  
Visual Attention Model ◽  
Attention Model

Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.

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