scholarly journals Camera-Based Blind Spot Detection with a General Purpose Lightweight Neural Network

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 233 ◽  
Author(s):  
Yiming Zhao ◽  
Lin Bai ◽  
Yecheng Lyu ◽  
Xinming Huang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Embedded System ◽  
High Speed ◽  
Blind Spot ◽  
General Purpose ◽  
Driver Assistance Systems ◽  
Spot Detection ◽  
Novel Method ◽  
Series Of Experiments

Blind spot detection is an important feature of Advanced Driver Assistance Systems (ADAS). In this paper, we provide a camera-based deep learning method that accurately detects other vehicles in the blind spot, replacing the traditional higher cost solution using radars. The recent breakthrough of deep learning algorithms shows extraordinary performance when applied to many computer vision tasks. Many new convolutional neural network (CNN) structures have been proposed and most of the networks are very deep in order to achieve the state-of-art performance when evaluated with benchmarks. However, blind spot detection, as a real-time embedded system application, requires high speed processing and low computational complexity. Hereby, we propose a novel method that transfers blind spot detection to an image classification task. Subsequently, a series of experiments are conducted to design an efficient neural network by comparing some of the latest deep learning models. Furthermore, we create a dataset with more than 10,000 labeled images using the blind spot view camera mounted on a test vehicle. Finally, we train the proposed deep learning model and evaluate its performance on the dataset.

scholarly journals A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1031
Author(s):  
Joseba Gorospe ◽  
Rubén Mulero ◽  
Olatz Arbelaitz ◽  
Javier Muguerza ◽  
Miguel Ángel Antón
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Embedded System ◽  
General Purpose ◽  
Learning Networks ◽  
Performance Study ◽  
Learning Techniques ◽  
Wide Range ◽  
Learning Architectures

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.

scholarly journals Recognition of Urdu Handwritten Characters Using Convolutional Neural Network

2019 ◽  
Vol 9 (13) ◽  
pp. 2758 ◽  
Author(s):  
Mujtaba Husnain ◽  
Malik Muhammad Saad Missen ◽  
Shahzad Mumtaz ◽  
Muhammad Zeeshan Jhanidr ◽  
Mickaël Coustaty ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Pattern Matching ◽  
Character Recognition ◽  
Learning Models ◽  
Series Of Experiments

In the area of pattern recognition and pattern matching, the methods based on deep learning models have recently attracted several researchers by achieving magnificent performance. In this paper, we propose the use of the convolutional neural network to recognize the multifont offline Urdu handwritten characters in an unconstrained environment. We also propose a novel dataset of Urdu handwritten characters since there is no publicly-available dataset of this kind. A series of experiments are performed on our proposed dataset. The accuracy achieved for character recognition is among the best while comparing with the ones reported in the literature for the same task.

scholarly journals Visual Interpretation of Convolutional Neural Network Predictions in Classifying Medical Image Modalities

Diagnostics ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 38 ◽  
Author(s):  
Incheol Kim ◽  
Sivaramakrishnan Rajaraman ◽  
Sameer Antani
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Convolutional Neural Network ◽  
Visual Information ◽  
Medical Image ◽  
Input Image ◽  
Visual Interpretation ◽  
Feature Maps ◽  
Novel Method ◽  
Series Of Experiments

Deep learning (DL) methods are increasingly being applied for developing reliable computer-aided detection (CADe), diagnosis (CADx), and information retrieval algorithms. However, challenges in interpreting and explaining the learned behavior of the DL models hinders their adoption and use in real-world systems. In this study, we propose a novel method called “Class-selective Relevance Mapping” (CRM) for localizing and visualizing discriminative regions of interest (ROI) within a medical image. Such visualizations offer improved explanation of the convolutional neural network (CNN)-based DL model predictions. We demonstrate CRM effectiveness in classifying medical imaging modalities toward automatically labeling them for visual information retrieval applications. The CRM is based on linear sum of incremental mean squared errors (MSE) calculated at the output layer of the CNN model. It measures both positive and negative contributions of each spatial element in the feature maps produced from the last convolution layer leading to correct classification of an input image. A series of experiments on a “multi-modality” CNN model designed for classifying seven different types of image modalities shows that the proposed method is significantly better in detecting and localizing the discriminative ROIs than other state of the art class-activation methods. Further, to visualize its effectiveness we generate “class-specific” ROI maps by averaging the CRM scores of images in each modality class, and characterize the visual explanation through their different size, shape, and location for our multi-modality CNN model that achieved over 98% performance on a dataset constructed from publicly available images.

scholarly journals Modelling a Spatial-Motion Deep Learning Framework to Classify Dynamic Patterns of Videos

2020 ◽  
Vol 10 (4) ◽  
pp. 1479 ◽  
Author(s):  
Sandeli Priyanwada Kasthuri Arachchi ◽  
Timothy K. Shih ◽  
Noorkholis Luthfil Hakim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Short Term Memory ◽  
General Purpose ◽  
Spatial Motion ◽  
Video Information ◽  
Network Concept ◽  
Motion Features ◽  
Dynamic Patterns ◽  
Fireworks Displays

Video classification is an essential process for analyzing the pervasive semantic information of video content in computer vision. Traditional hand-crafted features are insufficient when classifying complex video information due to the similarity of visual contents with different illumination conditions. Prior studies of video classifications focused on the relationship between the standalone streams themselves. In this paper, by leveraging the effects of deep learning methodologies, we propose a two-stream neural network concept, named state-exchanging long short-term memory (SE-LSTM). With the model of spatial motion state-exchanging, the SE-LSTM can classify dynamic patterns of videos using appearance and motion features. The SE-LSTM extends the general purpose of LSTM by exchanging the information with previous cell states of both appearance and motion stream. We propose a novel two-stream model Dual-CNNSELSTM utilizing the SE-LSTM concept combined with a Convolutional Neural Network, and use various video datasets to validate the proposed architecture. The experimental results demonstrate that the performance of the proposed two-stream Dual-CNNSELSTM architecture significantly outperforms other datasets, achieving accuracies of 81.62%, 79.87%, and 69.86% with hand gestures, fireworks displays, and HMDB51 datasets, respectively. Furthermore, the overall results signify that the proposed model is most suited to static background dynamic patterns classifications.

scholarly journals Optical coherent dot-product chip for sophisticated deep learning regression

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Shaofu Xu ◽  
Jing Wang ◽  
Haowen Shu ◽  
Zhike Zhang ◽  
Sicheng Yi ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Language Processing ◽  
High Speed ◽  
Autonomous Driving ◽  
Single Chip ◽  
Optical Fields ◽  
Large Bandwidth ◽  
Dot Product

AbstractOptical implementations of neural networks (ONNs) herald the next-generation high-speed and energy-efficient deep learning computing by harnessing the technical advantages of large bandwidth and high parallelism of optics. However, due to the problems of the incomplete numerical domain, limited hardware scale, or inadequate numerical accuracy, the majority of existing ONNs were studied for basic classification tasks. Given that regression is a fundamental form of deep learning and accounts for a large part of current artificial intelligence applications, it is necessary to master deep learning regression for further development and deployment of ONNs. Here, we demonstrate a silicon-based optical coherent dot-product chip (OCDC) capable of completing deep learning regression tasks. The OCDC adopts optical fields to carry out operations in the complete real-value domain instead of in only the positive domain. Via reusing, a single chip conducts matrix multiplications and convolutions in neural networks of any complexity. Also, hardware deviations are compensated via in-situ backpropagation control provided the simplicity of chip architecture. Therefore, the OCDC meets the requirements for sophisticated regression tasks and we successfully demonstrate a representative neural network, the AUTOMAP (a cutting-edge neural network model for image reconstruction). The quality of reconstructed images by the OCDC and a 32-bit digital computer is comparable. To the best of our knowledge, there is no precedent of performing such state-of-the-art regression tasks on ONN chips. It is anticipated that the OCDC can promote the novel accomplishment of ONNs in modern AI applications including autonomous driving, natural language processing, and scientific study.

scholarly journals Pop-In Identification in Nanoindentation Curves with Deep Learning Algorithms

Materials ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7027
Author(s):  
Stephania Kossman ◽  
Maxence Bigerelle
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
High Speed ◽  
Large Datasets ◽  
Loading Path ◽  
Load Displacement ◽  
Deep Learning Model ◽  
Data Analysis Methods

High–speed nanoindentation rapidly generates large datasets, opening the door for advanced data analysis methods such as the resources available in artificial intelligence. The present study addresses the problem of differentiating load–displacement curves presenting pop-in, slope changes, or instabilities from curves exhibiting a typical loading path in large nanoindentation datasets. Classification of the curves was achieved with a deep learning model, specifically, a convolutional neural network (CNN) model implemented in Python using TensorFlow and Keras libraries. Load–displacement curves (with pop-in and without pop-in) from various materials were input to train and validate the model. The curves were converted into square matrices (50 × 50) and then used as inputs for the CNN model. The model successfully differentiated between pop-in and non-pop-in curves with approximately 93% accuracy in the training and validation datasets, indicating that the risk of overfitting the model was negligible. These results confirmed that artificial intelligence and computer vision models represent a powerful tool for analyzing nanoindentation data.

scholarly journals A deep learning approach based hardware solution to categorise garbage in environment

2021 ◽  
Author(s):  
Tanya Gupta ◽  
Rakshit Joshi ◽  
Devarshi Mukhopadhyay ◽  
Kartik Sachdeva ◽  
Nikita Jain ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Embedded System ◽  
Image Classification ◽  
Urban Areas ◽  
Performance Measure ◽  
Raspberry Pi ◽  
Infrared Sensors ◽  
Base Level ◽  
Neural Network System

AbstractGarbage detection and disposal have become one of the major hassles in urban planning. Due to population influx in urban areas, the rate of garbage generation has increased exponentially along with garbage diversity. In this paper, we propose a hardware solution for garbage segregation at the base level based on deep learning architecture. The proposed deep-learning-based hardware solution SmartBin can segregate the garbage into biodegradable and non-biodegradable using Image classification through a Convolutional Neural Network System Architecture using a Real-time embedded system. Garbage detection via image classification aims for quick and efficient categorization of garbage present in the bin. However, this is an arduous task as garbage can be of any dimension, object, color, texture, unlike object detection of a particular entity where images of objects of that entity do share some similar characteristics and traits. The objective of this work is to compare the performance of various pre-trained Convolution Neural Network namely AlexNet, ResNet, VGG-16, and InceptionNet for garbage classification and test their working along with hardware components (PiCam, raspberry pi, infrared sensors, etc.) used for garbage detection in the bin. The InceptionNet Neural Network showed the best performance measure for the proposed model with an accuracy of 98.15% and a loss of 0.10 for the training set while it was 96.23% and 0.13 for the validation set.

scholarly journals Global Random Graph Convolution Network for Hyperspectral Image Classification

2021 ◽  
Vol 13 (12) ◽  
pp. 2285
Author(s):  
Chaozi Zhang ◽  
Jianli Wang ◽  
Kainan Yao
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Random Graph ◽  
Adjacency Matrix ◽  
Hyperspectral Image ◽  
Learning Methods ◽  
Current State ◽  
Benchmark Datasets ◽  
Novel Method ◽  
The Relationship

Machine learning and deep learning methods have been employed in the hyperspectral image (HSI) classification field. Of deep learning methods, convolution neural network (CNN) has been widely used and achieved promising results. However, CNN has its limitations in modeling sample relations. Graph convolution network (GCN) has been introduced to HSI classification due to its demonstrated ability in processing sample relations. Introducing GCN into HSI classification, the key issue is how to transform HSI, a typical euclidean data, into non-euclidean data. To address this problem, we propose a supervised framework called the Global Random Graph Convolution Network (GR-GCN). A novel method of constructing the graph is adopted for the network, where the graph is built by randomly sampling from the labeled data of each class. Using this technique, the size of the constructed graph is small, which can save computing resources, and we can obtain an enormous quantity of graphs, which also solves the problem of insufficient samples. Besides, the random combination of samples can make the generated graph more diverse and make the network more robust. We also use a neural network with trainable parameters, instead of artificial rules, to determine the adjacency matrix. An adjacency matrix obtained by a neural network is more flexible and stable, and it can better represent the relationship between nodes in a graph. We perform experiments on three benchmark datasets, and the results demonstrate that the GR-GCN performance is competitive with that of current state-of-the-art methods.

Sign in / Sign up

Export Citation Format

Share Document