scholarly journals A Deep Learning-Based Dirt Detection Computer Vision System for Floor-Cleaning Robots with Improved Data Collection

Technologies ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 94
Author(s):  
Daniel Canedo ◽  
Pedro Fonseca ◽  
Petia Georgieva ◽  
António J. R. Neves
Keyword(s):  
Vision System ◽  
Synthetic Data ◽  
Real Data ◽  
False Positives ◽  
Training Dataset ◽  
Novel Approach ◽  
Data Generator ◽  
Automation And Control ◽  
Cleaning Robots ◽  
Save Energy

Floor-cleaning robots are becoming increasingly more sophisticated over time and with the addition of digital cameras supported by a robust vision system they become more autonomous, both in terms of their navigation skills but also in their capabilities of analyzing the surrounding environment. This document proposes a vision system based on the YOLOv5 framework for detecting dirty spots on the floor. The purpose of such a vision system is to save energy and resources, since the cleaning system of the robot will be activated only when a dirty spot is detected and the quantity of resources will vary according to the dirty area. In this context, false positives are highly undesirable. On the other hand, false negatives will lead to a poor cleaning performance of the robot. For this reason, a synthetic data generator found in the literature was improved and adapted for this work to tackle the lack of real data in this area. This synthetic data generator allows for large datasets with numerous samples of floors and dirty spots. A novel approach in selecting floor images for the training dataset is proposed. In this approach, the floor is segmented from other objects in the image such that dirty spots are only generated on the floor and do not overlap those objects. This helps the models to distinguish between dirty spots and objects in the image, which reduces the number of false positives. Furthermore, a relevant dataset of the Automation and Control Institute (ACIN) was found to be partially labelled. Consequently, this dataset was annotated from scratch, tripling the number of labelled images and correcting some poor annotations from the original labels. Finally, this document shows the process of generating synthetic data which is used for training YOLOv5 models. These models were tested on a real dataset (ACIN) and the best model attained a mean average precision (mAP) of 0.874 for detecting solid dirt. These results further prove that our proposal is able to use synthetic data for the training step and effectively detect dirt on real data. According to our knowledge, there are no previous works reporting the use of YOLOv5 models in this application.

scholarly journals G-Tric: generating three-way synthetic datasets with triclustering solutions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
João Lobo ◽  
Rui Henriques ◽  
Sara C. Madeira
Keyword(s):  
State Of The Art ◽  
Synthetic Data ◽  
Ground Truth ◽  
Real Data ◽  
Three Dimensions ◽  
Additional Advantage ◽  
Urban Dynamics ◽  
Data Generator ◽  
Real World Datasets ◽  
Synthetic Datasets

Abstract Background Three-way data started to gain popularity due to their increasing capacity to describe inherently multivariate and temporal events, such as biological responses, social interactions along time, urban dynamics, or complex geophysical phenomena. Triclustering, subspace clustering of three-way data, enables the discovery of patterns corresponding to data subspaces (triclusters) with values correlated across the three dimensions (observations $$\times$$ × features $$\times$$ × contexts). With increasing number of algorithms being proposed, effectively comparing them with state-of-the-art algorithms is paramount. These comparisons are usually performed using real data, without a known ground-truth, thus limiting the assessments. In this context, we propose a synthetic data generator, G-Tric, allowing the creation of synthetic datasets with configurable properties and the possibility to plant triclusters. The generator is prepared to create datasets resembling real 3-way data from biomedical and social data domains, with the additional advantage of further providing the ground truth (triclustering solution) as output. Results G-Tric can replicate real-world datasets and create new ones that match researchers needs across several properties, including data type (numeric or symbolic), dimensions, and background distribution. Users can tune the patterns and structure that characterize the planted triclusters (subspaces) and how they interact (overlapping). Data quality can also be controlled, by defining the amount of missing, noise or errors. Furthermore, a benchmark of datasets resembling real data is made available, together with the corresponding triclustering solutions (planted triclusters) and generating parameters. Conclusions Triclustering evaluation using G-Tric provides the possibility to combine both intrinsic and extrinsic metrics to compare solutions that produce more reliable analyses. A set of predefined datasets, mimicking widely used three-way data and exploring crucial properties was generated and made available, highlighting G-Tric’s potential to advance triclustering state-of-the-art by easing the process of evaluating the quality of new triclustering approaches.

scholarly journals Why Temporal Persistence of Biometric Features, as Assessed by the Intraclass Correlation Coefficient, Is So Valuable for Classification Performance

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4555
Author(s):  
Lee Friedman ◽  
Hal S. Stern ◽  
Larry R. Price ◽  
Oleg V. Komogortsev
Keyword(s):  
Correlation Coefficient ◽  
Intraclass Correlation Coefficient ◽  
Intraclass Correlation ◽  
Synthetic Data ◽  
Real Data ◽  
Similarity Score ◽  
Classification Performance ◽  
Biometric Analysis ◽  
Novel Approach ◽  
Temporal Persistence

It is generally accepted that relatively more permanent (i.e., more temporally persistent) traits are more valuable for biometric performance than less permanent traits. Although this finding is intuitive, there is no current work identifying exactly where in the biometric analysis temporal persistence makes a difference. In this paper, we answer this question. In a recent report, we introduced the intraclass correlation coefficient (ICC) as an index of temporal persistence for such features. Here, we present a novel approach using synthetic features to study which aspects of a biometric identification study are influenced by the temporal persistence of features. What we show is that using more temporally persistent features produces effects on the similarity score distributions that explain why this quality is so key to biometric performance. The results identified with the synthetic data are largely reinforced by an analysis of two datasets, one based on eye-movements and one based on gait. There was one difference between the synthetic and real data, related to the intercorrelation of features in real data. Removing these intercorrelations for real datasets with a decorrelation step produced results which were very similar to that obtained with synthetic features.

scholarly journals Crash to Not Crash: Learn to Identify Dangerous Vehicles Using a Simulator

2019 ◽  
Vol 33 ◽  
pp. 978-985 ◽  
Author(s):  
Hoon Kim ◽  
Kangwook Lee ◽  
Gyeongjo Hwang ◽  
Changho Suh
Keyword(s):  
Driving Simulator ◽  
Synthetic Data ◽  
Real Data ◽  
Classification Performance ◽  
Motion Model ◽  
Time To Collision ◽  
Data Generator ◽  
Internal States ◽  
Accident Data

Developing a computer vision-based algorithm for identifying dangerous vehicles requires a large amount of labeled accident data, which is difficult to collect in the real world. To tackle this challenge, we first develop a synthetic data generator built on top of a driving simulator. We then observe that the synthetic labels that are generated based on simulation results are very noisy, resulting in poor classification performance. In order to improve the quality of synthetic labels, we propose a new label adaptation technique that first extracts internal states of vehicles from the underlying driving simulator, and then refines labels by predicting future paths of vehicles based on a well-studied motion model. Via real-data experiments, we show that our dangerous vehicle classifier can reduce the missed detection rate by at least 18.5% compared with those trained with real data when time-to-collision is between 1.6s and 1.8s.

scholarly journals Towards End-to-End Acoustic Localization Using Deep Learning: From Audio Signals to Source Position Coordinates

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3418 ◽  
Author(s):  
Juan Vera-Diaz ◽  
Daniel Pizarro ◽  
Javier Macias-Guarasa
Keyword(s):  
Three Dimensional ◽  
Synthetic Data ◽  
Audio Signal ◽  
Real Data ◽  
Audio Signals ◽  
Acoustic Source ◽  
Acoustic Localization ◽  
Training Strategy ◽  
Audio Features ◽  
Novel Approach

This paper presents a novel approach for indoor acoustic source localization using microphone arrays, based on a Convolutional Neural Network (CNN). In the proposed solution, the CNN is designed to directly estimate the three-dimensional position of a single acoustic source using the raw audio signal as the input information and avoiding the use of hand-crafted audio features. Given the limited amount of available localization data, we propose, in this paper, a training strategy based on two steps. We first train our network using semi-synthetic data generated from close talk speech recordings. We simulate the time delays and distortion suffered in the signal that propagate from the source to the array of microphones. We then fine tune this network using a small amount of real data. Our experimental results, evaluated on a publicly available dataset recorded in a real room, show that this approach is able to produce networks that significantly improve existing localization methods based on SRP-PHAT strategies and also those presented in very recent proposals based on Convolutional Recurrent Neural Networks (CRNN). In addition, our experiments show that the performance of our CNN method does not show a relevant dependency on the speaker’s gender, nor on the size of the signal window being used.

Migration in orthorhombic media: A prestack time-migration approach

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. C217-C227 ◽  
Author(s):  
Baoqing Tian ◽  
Jiangjie Zhang
Keyword(s):  
Synthetic Data ◽  
Real Data ◽  
High Resolution Imaging ◽  
Model Data ◽  
Data Set ◽  
Realistic Case ◽  
Time Migration ◽  
Novel Approach ◽  
Resolution Imaging ◽  
Prestack Time Migration

High-resolution imaging has become more popular recently in exploration geophysics. Conventionally, geophysicists image the subsurface using the isotropy approximation. When considering the anisotropy effects, one can expect to obtain an imaging profile with higher accuracy than the isotropy approach allows. Orthorhombic anisotropy is considered an ideal approximation in the realistic case. It has been used in the industry for several years. Although being attractive, broad application of orthorhombic anisotropy has many problems to solve. We have developed a novel approach of prestack time migration in the orthorhombic case. The traveltime and amplitude of a wave propagating in orthorhombic media are calculated directly by launching new anisotropic velocity and anisotropic parameters. We validate our methods with synthetic data. We also highlight our methods with model data set and real data. The results found that our methods work well for prestack time migration in orthorhombic media.

scholarly journals Automatic Inference of Demographic Parameters Using Generative Adversarial Networks

2020 ◽  
Author(s):  
Zhanpeng Wang ◽  
Jiaping Wang ◽  
Michael Kourakos ◽  
Nhung Hoang ◽  
Hyong Hark Lee ◽  
...  
Keyword(s):  
Synthetic Data ◽  
Simulated Data ◽  
Real Data ◽  
Simulation Software ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Isolation With Migration ◽  
Adversarial Network ◽  
Novel Approach ◽  
Input Parameters

AbstractPopulation genetics relies heavily on simulated data for validation, inference, and intuition. In particular, since real data is always limited, simulated data is crucial for training machine learning methods. Simulation software can accurately model evolutionary processes, but requires many hand-selected input parameters. As a result, simulated data often fails to mirror the properties of real genetic data, which limits the scope of methods that rely on it. In this work, we develop a novel approach to estimating parameters in population genetic models that automatically adapts to data from any population. Our method is based on a generative adversarial network that gradually learns to generate realistic synthetic data. We demonstrate that our method is able to recover input parameters in a simulated isolation-with-migration model. We then apply our method to human data from the 1000 Genomes Project, and show that we can accurately recapitulate the features of real data.

scholarly journals A Stochastic Model for Residential User Activity Simulation

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3326 ◽  
Author(s):  
Xiufeng Liu ◽  
Yanyan Yang ◽  
Rongling Li ◽  
Per Sieverts Nielsen
Keyword(s):  
Stochastic Model ◽  
Residential Buildings ◽  
Synthetic Data ◽  
Real Data ◽  
Small Sample ◽  
Fine Grained ◽  
Data Generator ◽  
Time Use Survey ◽  
And Performance ◽  
User Activities

User activities is an important input to energy modelling, simulation and performance studies of residential buildings. However, it is often difficult to obtain detailed data on user activities and related energy consumption data. This paper presents a stochastic model based on Markov chain to simulate user activities of the households with one or more family members, and formalizes the simulation processes under different conditions. A data generator is implemented to create fine-grained activity sequences that require only a small sample of time-use survey data as a seed. This paper evaluates the data generator by comparing the generated synthetic data with real data, and comparing other related work. The results show the effectiveness of the proposed modelling approach and the efficiency of generating realistic residential user activities.

scholarly journals Towards End-to-End Acoustic Localization Using Deep Learning: From Audio Signal to Source Position Coordinates

2018 ◽  
Author(s):  
Juan Manuel Vera-Diaz ◽  
Daniel Pizarro ◽  
Javier Macias-Guarasa
Keyword(s):  
Three Dimensional ◽  
Synthetic Data ◽  
Audio Signal ◽  
Real Data ◽  
Acoustic Source ◽  
Acoustic Localization ◽  
Training Strategy ◽  
Audio Features ◽  
Novel Approach ◽  
Acoustic Source Localization

This paper presents a novel approach for indoor acoustic source localization using microphone arrays and based on a Convolutional Neural Network (CNN). The proposed solution is, to the best of our knowledge, the first published work in which the CNN is designed to directly estimate the three dimensional position of an acoustic source, using the raw audio signal as the input information avoiding the use of hand crafted audio features. Given the limited amount of available localization data, we propose in this paper a training strategy based on two steps. We first train our network using semi-synthetic data, generated from close talk speech recordings, and where we simulate the time delays and distortion suffered in the signal that propagates from the source to the array of microphones. We then fine tune this network using a small amount of real data. Our experimental results show that this strategy is able to produce networks that significantly improve existing localization methods based on SRP-PHAT strategies. In addition, our experiments show that our CNN method exhibits better resistance against varying gender of the speaker and different window sizes compared with the other methods.

A multi-dimensional quality comparison of synthetic data generators (Preprint)

2021 ◽  
Author(s):  
Fida Dankar ◽  
Mahmoud K. Ibrahim ◽  
Leila Ismail
Keyword(s):  
Machine Learning ◽  
Synthetic Data ◽  
Quality Criteria ◽  
Real Data ◽  
Supervised Machine Learning ◽  
Specific Analysis ◽  
Data Generator ◽  
High Utility ◽  
Synthetic Datasets ◽  
Utility Metrics

BACKGROUND Synthetic datasets are gradually emerging as solutions for fast and inclusive health data sharing. Multiple synthetic data generators have been introduced in the last decade fueled by advancement in machine learning, yet their utility is not well understood. Few recent papers tried to compare the utility of synthetic data generators, each focused on different evaluation metrics and presented conclusions targeted at specific analysis. OBJECTIVE This work aims to understand the overall utility (referred to as quality) of four recent synthetic data generators by identifying multiple criteria for high-utility for synthetic data. METHODS We investigate commonly used utility metrics for masked data evaluation and classify them into criteria/categories depending on the function they attempt to preserve: attribute fidelity, bivariate fidelity, population fidelity, and application fidelity. Then we chose a representative metric from each of the identified categories based on popularity and consistency. The set of metrics together, referred to as quality criteria, are used to evaluate the overall utility of four recent synthetic data generators across 19 datasets of different sizes and feature counts. Moreover, correlations between the identified metrics are investigated in an attempt to streamline synthetic data utility. RESULTS Our results indicate that a non-parametric machine learning synthetic data generator (Synthpop) provides the best utility values across all quality criteria along with the highest stability. It displays the best overall accuracy in supervised machine learning and often agrees with real dataset on the learning model with the highest accuracy. On another front, our results suggest no strong correlation between the different metrics, which implies that all categories/dimensions are required when evaluating the overall utility of synthetic data. CONCLUSIONS The paper used four quality criteria to inform on the synthesizer with the best overall utility. The results are promising with small decreases in accuracy observed from the winning synthesizer when tested with real datasets (in comparison with models trained on real data). Further research into one (overall) quality measure would greatly help data holders in optimizing the utility of the released dataset.

scholarly journals The hidden waves in the ECG uncovered revealing a sound automated interpretation method

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Cristina Rueda ◽  
Yolanda Larriba ◽  
Adrian Lamela
Keyword(s):  
Error Term ◽  
Synthetic Data ◽  
Real Data ◽  
Black Box ◽  
Reference Points ◽  
Electric System ◽  
Novel Approach ◽  
Automated Interpretation ◽  
Interpretation Method ◽  
New Discovery

AbstractA novel approach for analysing cardiac rhythm data is presented in this paper. Heartbeats are decomposed into the five fundamental P, Q, R, S and T waves plus an error term to account for artifacts in the data which provides a meaningful, physical interpretation of the heart’s electric system. The morphology of each wave is concisely described using four parameters that allow all the different patterns in heartbeats to be characterized and thus differentiated This multi-purpose approach solves such questions as the extraction of interpretable features, the detection of the fiducial marks of the fundamental waves, or the generation of synthetic data and the denoising of signals. Yet the greatest benefit from this new discovery will be the automatic diagnosis of heart anomalies as well as other clinical uses with great advantages compared to the rigid, vulnerable and black box machine learning procedures, widely used in medical devices. The paper shows the enormous potential of the method in practice; specifically, the capability to discriminate subjects, characterize morphologies and detect the fiducial marks (reference points) are validated numerically using simulated and real data, thus proving that it outperforms its competitors.

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