A method of locating the 3D centers of retroreflectors based on deep learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
BinBin Zhang ◽  
Fumin Zhang ◽  
Xinghua Qu
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Measurement Techniques ◽  
Recognition Rate ◽  
Detection Algorithm ◽  
Large Field ◽  
Small Scale ◽  
Data Set ◽  
Content Type ◽  
Ellipse Detection

Purpose Laser-based measurement techniques offer various advantages over conventional measurement techniques, such as no-destructive, no-contact, fast and long measuring distance. In cooperative laser ranging systems, it’s crucial to extract center coordinates of retroreflectors to accomplish automatic measurement. To solve this problem, this paper aims to propose a novel method. Design/methodology/approach We propose a method using Mask RCNN (Region Convolutional Neural Network), with ResNet101 (Residual Network 101) and FPN (Feature Pyramid Network) as the backbone, to localize retroreflectors, realizing automatic recognition in different backgrounds. Compared with two other deep learning algorithms, experiments show that the recognition rate of Mask RCNN is better especially for small-scale targets. Based on this, an ellipse detection algorithm is introduced to obtain the ellipses of retroreflectors from recognized target areas. The center coordinates of retroreflectors in the camera coordinate system are obtained by using a mathematics method. Findings To verify the accuracy of this method, an experiment was carried out: the distance between two retroreflectors with a known distance of 1,000.109 mm was measured, with 2.596 mm root-mean-squar error, meeting the requirements of the coarse location of retroreflectors. Research limitations/implications The research limitations/implications are as follows: (i) As the data set only has 200 pictures, although we have used some data augmentation methods such as rotating, mirroring and cropping, there is still room for improvement in the generalization ability of detection. (ii) The ellipse detection algorithm needs to work in relatively dark conditions, as the retroreflector is made of stainless steel, which easily reflects light. Originality/value The originality/value of the article lies in being able to obtain center coordinates of multiple retroreflectors automatically even in a cluttered background; being able to recognize retroreflectors with different sizes, especially for small targets; meeting the recognition requirement of multiple targets in a large field of view and obtaining 3 D centers of targets by monocular model-based vision.

scholarly journals Pedestrian detection algorithm in traffic scene based on weakly supervised hierarchical deep model

2016 ◽  
Vol 14 (1) ◽  
pp. 172988141769231 ◽  
Author(s):  
Yingfeng Cai ◽  
Youguo He ◽  
Hai Wang ◽  
Xiaoqiang Sun ◽  
Long Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pedestrian Detection ◽  
Recognition Rate ◽  
Detection Algorithm ◽  
Training Methods ◽  
Two Dimensional ◽  
Data Set ◽  
Deep Model ◽  
Unsupervised Training ◽  
Weakly Supervised

The emergence and development of deep learning theory in machine learning field provide new method for visual-based pedestrian recognition technology. To achieve better performance in this application, an improved weakly supervised hierarchical deep learning pedestrian recognition algorithm with two-dimensional deep belief networks is proposed. The improvements are made by taking into consideration the weaknesses of structure and training methods of existing classifiers. First, traditional one-dimensional deep belief network is expanded to two-dimensional that allows image matrix to be loaded directly to preserve more information of a sample space. Then, a determination regularization term with small weight is added to the traditional unsupervised training objective function. By this modification, original unsupervised training is transformed to weakly supervised training. Subsequently, that gives the extracted features discrimination ability. Multiple sets of comparative experiments show that the performance of the proposed algorithm is better than other deep learning algorithms in recognition rate and outperforms most of the existing state-of-the-art methods in non-occlusion pedestrian data set while performs fair in weakly and heavily occlusion data set.

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

scholarly journals Effects of agricultural credit on wheat productivity of small farms in Sindh, Pakistan

2018 ◽  
Vol 78 (5) ◽  
pp. 592-610 ◽  
Author(s):  
Abbas Ali Chandio ◽  
Yuansheng Jiang ◽  
Feng Wei ◽  
Xu Guangshun
Keyword(s):  
Wheat Yield ◽  
Considerable Proportion ◽  
Small Scale ◽  
Small Farms ◽  
Short Term ◽  
Agricultural Credit ◽  
Data Set ◽  
Content Type ◽  
The Impact

Purpose The purpose of this paper is to evaluate the impact of short-term loan (STL) vs long-term loan (LTL) on wheat productivity of small farms in Sindh, Pakistan. Design/methodology/approach The econometric estimation is based on cross-sectional data collected in 2016 from 18 villages in three districts, i.e. Shikarpur, Sukkur and Shaheed Benazirabad, Sindh, Pakistan. The sample data set consist of 180 wheat farmers. The collected data were analyzed through different econometric techniques like Cobb–Douglas production function and Instrumental variables (two-stage least squares) approach. Findings This study reconfirmed that agricultural credit has a positive and highly significant effect on wheat productivity, while the short-term loan has a stronger effect on wheat productivity than the long-term loan. The reasons behind the phenomenon may be the significantly higher usage of agricultural inputs like seeds of improved variety and fertilizers which can be transformed into the wheat yield in the same year. However, the LTL users have significantly higher investments in land preparation, irrigation and plant protection, which may lead to higher wheat production in the coming years. Research limitations/implications In the present study, only those wheat farmers were considered who obtained agricultural loans from formal financial institutions like Zarai Taraqiati Bank Limited and Khushhali Bank. However, in the rural areas of Sindh, Pakistan, a considerable proportion of small-scale farmers take credit from informal financial channels. Therefore future researchers should consider the informal credits as well. Originality/value This is the first paper to examine the effects of agricultural credit on wheat productivity of small farms in Sindh, Pakistan. This paper will be an important addition to the emerging literature regarding effects of credit studies.

Deep learning-based approach for segmentation of glioma sub-regions in MRI

2020 ◽  
Vol 13 (4) ◽  
pp. 389-406
Author(s):  
Jiten Chaudhary ◽  
Rajneesh Rani ◽  
Aman Kamboj
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Data Augmentation ◽  
Treatment Plan ◽  
Superior Performance ◽  
Content Type ◽  
Proposed Model ◽  
Life Threatening ◽  
Tumor Region ◽  
Magnetic Resonance Imaging Mri

PurposeBrain tumor is one of the most dangerous and life-threatening disease. In order to decide the type of tumor, devising a treatment plan and estimating the overall survival time of the patient, accurate segmentation of tumor region from images is extremely important. The process of manual segmentation is very time-consuming and prone to errors; therefore, this paper aims to provide a deep learning based method, that automatically segment the tumor region from MR images.Design/methodology/approachIn this paper, the authors propose a deep neural network for automatic brain tumor (Glioma) segmentation. Intensity normalization and data augmentation have been incorporated as pre-processing steps for the images. The proposed model is trained on multichannel magnetic resonance imaging (MRI) images. The model outputs high-resolution segmentations of brain tumor regions in the input images.FindingsThe proposed model is evaluated on benchmark BRATS 2013 dataset. To evaluate the performance, the authors have used Dice score, sensitivity and positive predictive value (PPV). The superior performance of the proposed model is validated by training very popular UNet model in the similar conditions. The results indicate that proposed model has obtained promising results and is effective for segmentation of Glioma regions in MRI at a clinical level.Practical implicationsThe model can be used by doctors to identify the exact location of the tumorous region.Originality/valueThe proposed model is an improvement to the UNet model. The model has fewer layers and a smaller number of parameters in comparison to the UNet model. This helps the network to train over databases with fewer images and gives superior results. Moreover, the information of bottleneck feature learned by the network has been fused with skip connection path to enrich the feature map.

scholarly journals Image editing-based data augmentation for illumination-insensitive background subtraction

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dimitrios Sakkos ◽  
Edmond S. L. Ho ◽  
Hubert P. H. Shum ◽  
Garry Elvin
Keyword(s):  
Deep Learning ◽  
Pilot Study ◽  
Background Subtraction ◽  
Data Augmentation ◽  
Point Of View ◽  
Content Type ◽  
Illumination Changes ◽  
Image Appearance ◽  
Using Data ◽  
Limited Training Samples

PurposeA core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames. In our pilot study published in, Sakkos:SKIMA 2019, we tackle the problem from a data point-of-view using data augmentation. Our method performs data augmentation that not only creates endless data on the fly but also features semantic transformations of illumination which enhance the generalisation of the model.Design/methodology/approachIn our pilot study published in SKIMA 2019, the proposed framework successfully simulates flashes and shadows by applying the Euclidean distance transform over a binary mask generated randomly. In this paper, we further enhance the data augmentation framework by proposing new variations in image appearance both locally and globally.FindingsExperimental results demonstrate the contribution of the synthetics in the ability of the models to perform BGS even when significant illumination changes take place.Originality/valueSuch data augmentation allows us to effectively train an illumination-invariant deep learning model for BGS. We further propose a post-processing method that removes noise from the output binary map of segmentation, resulting in a cleaner, more accurate segmentation map that can generalise to multiple scenes of different conditions. We show that it is possible to train deep learning models even with very limited training samples. The source code of the project is made publicly available at https://github.com/dksakkos/illumination_augmentation

Disaster communication

2019 ◽  
Vol 2 (3) ◽  
pp. 241-259
Author(s):  
Alicia Mason ◽  
Lynzee Flores ◽  
Pan Liu ◽  
Kenzie Tims ◽  
Elizabeth Spencer ◽  
...  
Keyword(s):  
Social Media ◽  
Crisis Communication ◽  
Medical Tourism ◽  
Tourism Industry ◽  
Small Scale ◽  
Data Set ◽  
Content Type ◽  
Disaster Communication ◽  
The Caribbean ◽  
Post Disaster

Purpose The purpose of this paper is to understand the crisis communication strategies used by the Caribbean medical tourism industry in the 2017 hurricane season, and also evaluate the quality of the disaster communication messages delivered via digital mediums. Design/methodology/approach This study includes a comprehensive, qualitative content analysis of 149 risk and crisis messages from 51 healthcare organizations distributed through digital media. The medical tourism providers (MTPs) include hospitals, medical tourism facilitators, practitioners/private physicians, specialty clinics, and dental and cosmetic providers. Findings Nearly half of the MTPs included in the data set delivered no post-disaster information to external audiences. The most prominent post-disaster message strategy utilized was conveying operational messages. Furthermore, an unexpected finding was the sheer magnitude of unrelated health-oriented and promotional destination marketing content disseminated before, during and after these events. Research limitations/implications This analysis excludes internal organizational channels of communication which may have been used to communicate risk and crisis messages during these events (i.e. employee e-mails, announcements made through intercom systems, etc.). Our analysis does not include content disseminated through medical tourism forums (i.e. Realself.com, Health Traveler’s Forum, FlyerTalk Forum). Practical implications Small-scale MTPs can improve on any weaknesses through proactive planning and preparation by creating organizational goals to complete basic crisis communication training courses and in doing so support the applied professional development of disaster and crisis responders in the Caribbean region. Second, MTPs exposed to similar risks of natural disasters may use these findings for comparative analysis purposes to support their own organizational planning. Finally, this study supports the continued utility of the National Center for Food Protection & Defense guidelines for analyzing and evaluating organizational performance. Originality/value Currently much of the academic scholarship of applied disaster communication narrowly focuses on the response strategies of one organization, or analyzes one social media platform at a time (i.e. Twitter). A strength of this analysis is the inclusion of an organizational sector (i.e. Caribbean medical tourism providers) and the range of platforms from which the content was captured (e.g. websites, org. blogs and social media networks).

Who and what set the price of walnuts for small-scale farmers in Southwest China?

2017 ◽  
Vol 7 (2) ◽  
pp. 135-152 ◽  
Author(s):  
Mei Yan ◽  
Anne Terheggen ◽  
Dagmar Mithöfer
Keyword(s):  
Value Chain ◽  
Food Systems ◽  
Tree Planting ◽  
Small Scale ◽  
Structured Interviews ◽  
Data Set ◽  
Content Type ◽  
Village Level ◽  
Small Scale Farmers ◽  
The Village

Purpose Domestic demand for walnuts has been on the rise for the last decades. Consumption outstrips domestic production capacities, which led to increasing prices until recently. Small-scale farmers are at the centre of walnut tree planting and walnut collection efforts. Farmers are now integrated into rapidly expanding agrifood value chains. The purpose of this paper is to investigate the walnut value chain originating in Yunnan (the dominant producer of walnuts in China). The authors are especially interested in the position of small-scale farmers in the chain and the factors affecting the price that they receive. Design/methodology/approach Price and intra-chain governance information were collected through structured interviews with value chain actors like certified and conventional small-scale farmers, traders, processors, food manufacturers and wholesalers. The resultant price data set was analysed using a multiple regression analysis. Findings Timing of harvest, distance to market and sales volume are correlated with the village-level price. Farmers are in a market governance segment of the chain. Lead firms (e.g. supermarkets) are price-setters and determine the value distribution, with farmers receiving a smaller share relative to downstream actors’ shares. Research limitations/implications Improved connectivity to markets, transparency of standards and price (formation), processing and certification could improve farmers’ profits. Originality/value The authors contribute to the growing literature of value chain studies focussing on farmers’ integration into food systems at different scales. The authors investigated the price determinants at the village level and additionally provide information on an organic marketing arrangement.

Welfare implications of intertemporal marketing margin manipulation

2017 ◽  
Vol 119 (8) ◽  
pp. 1656-1671 ◽  
Author(s):  
Thomas Kopp ◽  
Bernhard Brümmer ◽  
Zulkifli Alamsyah ◽  
Raja Sharah Fatricia
Keyword(s):  
Value Chain ◽  
Crumb Rubber ◽  
Daily Basis ◽  
Parametric Estimation ◽  
Price Transmission ◽  
Small Scale ◽  
Data Set ◽  
Content Type ◽  
Subsequent Step ◽  
Welfare Implications

Purpose In Indonesia, rubber is the most valuable export crop produced by small scale agriculture and plays a key role for inclusive economic development. This potential is likely to be not fully exploited. The observed concentration in the crumb rubber processing industry raises concerns about the distribution of export earnings along the value chain. Asymmetric price transmission (APT) is observed. The paper aims to discuss these issues. Design/methodology/approach This study investigates the price transmission between international prices and the factories’ purchasing prices on a daily basis. An auto-regressive asymmetric error correction model is estimated to find evidence for APT. In a subsequent step the rents that are redistributed from factories to farmers are calculated. The study then provides estimations of the size of this redistribution under different scenarios. Findings The results suggest that factories do indeed transmit prices asymmetrically, which has substantial welfare implications: around USD3 million are annually redistributed from farmers to factories. If the price transmission was only half as asymmetric as it is observed, the majority of this redistribution was re-diverted. Originality/value This study combines the approaches of non-parametric and parametric estimation techniques of estimating APT processes with a welfare perspective to quantify the distributional consequences of this intertemporal marketing margin manipulation. Especially the calculation of different scenarios of alternative price transmissions is a novelty. The data set of prices on such a disaggregated level and high frequency as required by this approach is also unique.

scholarly journals Football Players’ Shooting Posture Norm Based on Deep Learning in Sports Event Video

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Guangliang Huang ◽  
Zhuangxu Lan ◽  
Guo Huang
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Motion Tracking ◽  
Research Direction ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Target Motion ◽  
Football Players ◽  
Data Set ◽  
Offensive Tactics

Football is one of the favorite sports of people nowadays. Shooting is the ultimate goal of all offensive tactics in football matches. This is the most basic way to score a goal and the only way to score a goal. The choice and use of shooting technical indicators can have a great impact on the final result of the game. Therefore, how to improve the shooting technique of football players and how to adjust the shooting posture of football players are important issues faced by coaches and athletes. In recent years, deep learning has been widely used in various fields such as image classification and recognition and language processing. How to apply deep learning optimization to shooting gesture recognition is a very promising research direction. This article aims to study the football player’s shooting posture specification based on deep learning in sports event videos. Based on the analysis of target motion detection algorithm, target motion tracking algorithm, target motion recognition algorithm, and football shooting posture classification, KTH and Weizmann data sets are used. As the experimental verification data set of this article, the shooting posture of football players in the sports event video is recognized, and the accuracy of the action recognition is finally calculated to standardize the football shooting posture. The experimental results show that the Weizmann data set has a higher accuracy rate than the KTH data set and is more suitable for shooting attitude specifications.

scholarly journals Deep Learning based Arrhythmia Classification with an ECG Acquisition System

2019 ◽  
Vol 9 (2) ◽  
pp. 3849-3852
Keyword(s):  
Deep Learning ◽  
Premature Ventricular Contraction ◽  
Detection Algorithm ◽  
Raspberry Pi ◽  
Data Set ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
Peak Detection Algorithm ◽  
Different Types ◽  
Testing Accuracy

One of the issues that the human body faces is arrhythmia, a condition where the human heartbeat is either irregular, too slow or too fast. One of the ways to diagnose arrhythmia is by using ECG signals, the best diagnostic tool for detection of arrhythmia. This paper describes a deep learning approach to check whether signs of arrhythmia, in a given input signal, are present or not. A batch normalized CNN is used to classify the ECG signals based on the different types of arrhythmia. The model has achieved 96.39% training accuracy and 97% testing accuracy. The ECG signals are classified into five classes namely: Normal beats, Premature Ventricular Contraction (PVC) beats, Right Bundle Branch Block (RBBB) beats, Left Bundle Branch Block (LBBB) beats and Paced beats. A peak detection algorithm with six simple steps is designed to detect R-peaks from the ECG signals. A hardware device is built using Raspberry Pi to acquire ECG signals, which are then sent to the trained CNN for classification. The data-set for training is obtained from the MIT-BIH repository. Keras and Tensorflow libraries are used to design and develop the CNN and an application is designed using ’MEAN’ stack and ’Flask’ based servers.

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