CAD-to-real: enabling deep neural networks for 3D pose estimation of electronic control units

2021 ◽  
Vol 69 (10) ◽  
pp. 880-891
Author(s):  
Simon Bäuerle ◽  
Moritz Böhland ◽  
Jonas Barth ◽  
Markus Reischl ◽  
Andreas Steimer ◽  
...  
Keyword(s):  
Neural Networks ◽  
Pose Estimation ◽  
Synthetic Data ◽  
Training Data ◽  
Learning Approaches ◽  
Electronic Control ◽  
3D Pose Estimation ◽  
Visual Diversity ◽  
Control Units ◽  
Processing Techniques

Abstract Image processing techniques are widely used within automotive series production, including production of electronic control units (ECUs). Deep learning approaches have made rapid advances during the last years, but are not prominent in those industrial settings yet. One major obstacle is the lack of suitable training data. We adapt the recently developed method of domain randomization to our use case of 3D pose estimation of ECU housings. We create purely synthetic data with high visual diversity to train artificial neural networks (ANNs). This enables ANNs to estimate the 3D pose of a real sample part with high accuracy from a single low-resolution RGB image in a production-like setting. Requirements regarding measurement hardware are very low. Our entire setup is fully automated and can be transferred to related industrial use cases.

scholarly journals Neuron Connectivity and Thickness Analysis of Brain for Autism Spectrum Disorder to Improve Speed and Accuracy

2019 ◽  
Vol 9 (1) ◽  
pp. 20-25
Keyword(s):  
Neural Networks ◽  
Autism Spectrum Disorder ◽  
Cochlear Implants ◽  
Pose Estimation ◽  
Autism Spectrum ◽  
Speed Ratio ◽  
Neuronal Connectivity ◽  
3D Pose Estimation ◽  
Implanted Electrodes ◽  
Speed And Accuracy

Neuronal Connectivity is learning from the intelligence to enhance the knowledge of our computing devices, certain, namely recognition, locomotion, or objective recognition. Such synthetic neural networks have at last being used after understood patterns on talent recreation between Amygdala imaging Scientists studied the talent for 150 years, trying to link the intelligence along behavior. Such studies have old strategies beyond microscopes according to inserting genes within existing cells. This paper interface device, such so cochlear implants then implanted electrodes according to allow Amygdala Images according to pace devices outside perform repair lost applications to individuals. Neurons firing round 5 in imitation of 50 instances a second speed Signals in a tent about a second regular neuron makes 10000 connections including 5000 trillion synapses. The reliability propriety over susen algorithms that new method 3D pose estimation in Drosophila the usage on accuracy with speed ratio then statistics dividing in accordance with permit counterpart throughout analysis NIAK for UCI Dataset Autism Screening Adult(ASA) better rate of accuracy 95.41% and speed 91.72%.

scholarly journals Applying Deep Neural Networks and Ensemble Machine Learning Methods to Forecast Airborne Ambrosia Pollen

2019 ◽  
Vol 16 (11) ◽  
pp. 1992 ◽  
Author(s):  
Gebreab K. Zewdie ◽  
David J. Lary ◽  
Estelle Levetin ◽  
Gemechu F. Garuma
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Land Surface ◽  
Deep Neural Networks ◽  
Airborne Pollen ◽  
Training Data ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
Ambrosia Pollen ◽  
Extreme Gradient Boosting

Allergies to airborne pollen are a significant issue affecting millions of Americans. Consequently, accurately predicting the daily concentration of airborne pollen is of significant public benefit in providing timely alerts. This study presents a method for the robust estimation of the concentration of airborne Ambrosia pollen using a suite of machine learning approaches including deep learning and ensemble learners. Each of these machine learning approaches utilize data from the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric weather and land surface reanalysis. The machine learning approaches used for developing a suite of empirical models are deep neural networks, extreme gradient boosting, random forests and Bayesian ridge regression methods for developing our predictive model. The training data included twenty-four years of daily pollen concentration measurements together with ECMWF weather and land surface reanalysis data from 1987 to 2011 is used to develop the machine learning predictive models. The last six years of the dataset from 2012 to 2017 is used to independently test the performance of the machine learning models. The correlation coefficients between the estimated and actual pollen abundance for the independent validation datasets for the deep neural networks, random forest, extreme gradient boosting and Bayesian ridge were 0.82, 0.81, 0.81 and 0.75 respectively, showing that machine learning can be used to effectively forecast the concentrations of airborne pollen.

Traffic Sign Recognition Using a Synthetic Data Training Approach

2020 ◽  
Vol 29 (05) ◽  
pp. 2050013
Author(s):  
Oualid Araar ◽  
Abdenour Amamra ◽  
Asma Abdeldaim ◽  
Ivan Vitanov
Keyword(s):  
Neural Networks ◽  
Synthetic Data ◽  
Real Data ◽  
Training Data ◽  
Traffic Sign Recognition ◽  
Driver Assistance ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Crucial Component ◽  
Training Approach

Traffic Sign Recognition (TSR) is a crucial component in many automotive applications, such as driver assistance, sign maintenance, and vehicle autonomy. In this paper, we present an efficient approach to training a machine learning-based TSR solution. In our choice of recognition method, we have opted for convolutional neural networks, which have demonstrated best-in-class performance in previous works on TSR. One of the challenges related to training deep neural networks is the requirement for a large amount of training data. To circumvent the tedious process of acquiring and manually labelling real data, we investigate the use of synthetically generated images. Our networks, trained on only synthetic data, are capable of recognising traffic signs in challenging real-world footage. The classification results achieved on the GTSRB benchmark are seen to outperform existing state-of-the-art solutions.

scholarly journals USING SIMULATION DATA FROM GAMING ENVIRONMENTS FOR TRAINING A DEEP LEARNING ALGORITHM ON 3D POINT CLOUDS

2021 ◽  
Vol VIII-4/W2-2021 ◽  
pp. 67-74
Author(s):  
S. Spiegel ◽  
J. Chen
Keyword(s):  
Neural Networks ◽  
Real World ◽  
Resource Constraints ◽  
Learning Algorithm ◽  
Point Clouds ◽  
Training Data ◽  
Lidar Data ◽  
Learning Approaches ◽  
Data Set ◽  
3D Point Clouds

Abstract. Deep neural networks (DNNs) and convolutional neural networks (CNNs) have demonstrated greater robustness and accuracy in classifying two-dimensional images and three-dimensional point clouds compared to more traditional machine learning approaches. However, their main drawback is the need for large quantities of semantically labeled training data sets, which are often out of reach for those with resource constraints. In this study, we evaluated the use of simulated 3D point clouds for training a CNN learning algorithm to segment and classify 3D point clouds of real-world urban environments. The simulation involved collecting light detection and ranging (LiDAR) data using a simulated 16 channel laser scanner within the the CARLA (Car Learning to Act) autonomous vehicle gaming environment. We used this labeled data to train the Kernel Point Convolution (KPConv) and KPConv Segmentation Network for Point Clouds (KP-FCNN), which we tested on real-world LiDAR data from the NPM3D benchmark data set. Our results showed that high accuracy can be achieved using data collected in a simulator.

Learning time-frequency mask for noisy speech enhancement using gaussian-bernoulli pre-trained deep neural networks

2021 ◽  
Vol 40 (1) ◽  
pp. 849-864
Author(s):  
Nasir Saleem ◽  
Muhammad Irfan Khattak ◽  
Mu’ath Al-Hasan ◽  
Atif Jan
Keyword(s):  
Neural Networks ◽  
Speech Enhancement ◽  
Speech Intelligibility ◽  
Deep Neural Networks ◽  
Training Data ◽  
Learning Approaches ◽  
Performance Gain ◽  
Noisy Speech ◽  
Time Frequency ◽  
Training Scheme

Speech enhancement is a very important problem in various speech processing applications. Recently, supervised speech enhancement using deep learning approaches to estimate a time-frequency mask have proved remarkable performance gain. In this paper, we have proposed time-frequency masking-based supervised speech enhancement method for improving intelligibility and quality of the noisy speech. We believe that a large performance gain can be achieved if deep neural networks (DNNs) are layer-wise pre-trained by stacking Gaussian-Bernoulli Restricted Boltzmann Machine (GB-RBM). The proposed DNN is called as Gaussian-Bernoulli Deep Belief Network (GB-DBN) and are optimized by minimizing errors between the estimated and pre-defined masks. Non-linear Mel-Scale weighted mean square error (LMW-MSE) loss function is used as training criterion. We have examined the performance of the proposed pre-training scheme using different DNNs which are established on three time-frequency masks comprised of the ideal amplitude mask (IAM), ideal ratio mask (IRM), and phase sensitive mask (PSM). The results in different noisy conditions demonstrated that when DNNs are pre-trained by the proposed scheme provided a persistent performance gain in terms of the perceived speech intelligibility and quality. Also, the proposed pre-training scheme is effective and robust in noisy training data.

scholarly journals Automatic Defects Segmentation and Identification by Deep Learning Algorithm with Pulsed Thermography: Synthetic and Experimental Data

2021 ◽  
Vol 5 (1) ◽  
pp. 9
Author(s):  
Qiang Fang ◽  
Clemente Ibarra-Castanedo ◽  
Xavier Maldague
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Deep Learning ◽  
Infrared Thermography ◽  
Learning Algorithm ◽  
Synthetic Data ◽  
Training Data ◽  
Experimental Database ◽  
The Neural Network

In quality evaluation (QE) of the industrial production field, infrared thermography (IRT) is one of the most crucial techniques used for evaluating composite materials due to the properties of low cost, fast inspection of large surfaces, and safety. The application of deep neural networks tends to be a prominent direction in IRT Non-Destructive Testing (NDT). During the training of the neural network, the Achilles heel is the necessity of a large database. The collection of huge amounts of training data is the high expense task. In NDT with deep learning, synthetic data contributing to training in infrared thermography remains relatively unexplored. In this paper, synthetic data from the standard Finite Element Models are combined with experimental data to build repositories with Mask Region based Convolutional Neural Networks (Mask-RCNN) to strengthen the neural network, learning the essential features of objects of interest and achieving defect segmentation automatically. These results indicate the possibility of adapting inexpensive synthetic data merging with a certain amount of the experimental database for training the neural networks in order to achieve the compelling performance from a limited collection of the annotated experimental data of a real-world practical thermography experiment.

Sign in / Sign up

Export Citation Format

Share Document