scholarly journals Deep Binary Classification via Multi-Resolution Network and Stochastic Orthogonality for Subcompact Vehicle Recognition

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2715
Author(s):  
Joongchol Shin ◽  
Bonseok Koo ◽  
Yeongbin Kim ◽  
Joonki Paik
Keyword(s):  
Binary Classification ◽  
General Pattern ◽  
Input Image ◽  
Classification Systems ◽  
Vehicle Classification ◽  
Vehicle Recognition ◽  
Toll Road ◽  
Proposed Model ◽  
Save Energy

To encourage people to save energy, subcompact cars have several benefits of discount on parking or toll road charge. However, manual classification of the subcompact car is highly labor intensive. To solve this problem, automatic vehicle classification systems are good candidates. Since a general pattern-based classification technique can not successfully recognize the ambiguous features of a vehicle, we present a new multi-resolution convolutional neural network (CNN) and stochastic orthogonal learning method to train the network. We first extract the region of a bonnet in the vehicle image. Next, both extracted and input image are engaged to low and high resolution layers in the CNN model. The proposed network is then optimized based on stochastic orthogonality. We also built a novel subcompact vehicle dataset that will be open for a public use. Experimental results show that the proposed model outperforms state-of-the-art approaches in term of accuracy, which means that the proposed method can efficiently classify the ambiguous features between subcompact and non-subcompact vehicles.

Alternative Automatic Vehicle Classification Method

2010 ◽  
Vol 17 (3) ◽  
pp. 323-332 ◽  
Author(s):  
Piotr Burnos
Keyword(s):  
Characteristic Feature ◽  
High Selectivity ◽  
Classification Method ◽  
Vehicle Classification ◽  
Test Results ◽  
Open Structure ◽  
High Effectiveness ◽  
Vehicle Recognition ◽  
Fusion Methods

Alternative Automatic Vehicle Classification Method The paper deals with the new method of automatic vehicle classification called ALT (ALTernative). Its characteristic feature is versatility resulting from its open structure, moreover a user can adjust the number of vehicles and their category according to individual requirements. It uses an algorithm for automatic vehicle recognition employing data fusion methods and fuzzy sets. High effectiveness of classification while retaining high selectivity of division was proved by test results. The effectiveness of classification of all vehicles at the level of 95% and goods trucks of 100% is more than satisfactory.

A Kind of ECC - KNN Classifier's Vehicle Identification Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 3724-3727
Author(s):  
Lin Tao Lü ◽  
Huan Gao ◽  
Yu Xiang Yang
Keyword(s):  
Recognition Performance ◽  
Binary Classification ◽  
Recognition Algorithm ◽  
Identification Algorithm ◽  
Classification Problems ◽  
Vehicle Recognition ◽  
Vehicle Identification ◽  
Image Characteristics ◽  
Multi Class Classification

this article presents an improved classifier vehicle identification algorithm to improve the efficiency of the existing vehicle recognition algorithm. First, using edge orientation histograms to extract image characteristics, then, Error correction coding is applied to the classification of classifier, the multi-class classification problems turned into multiple binary classification problems. A large number of experimental analysis shows that the improved vehicle identification algorithm has good recognition performance and robustness. Therefore, the algorithm which this article used has high theoretical and practical value.

scholarly journals Bangladeshi Native Vehicle Classification Based on Transfer Learning with Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7545
Author(s):  
Md Mahibul Hasan ◽  
Zhijie Wang ◽  
Muhammad Ather Iqbal Hussain ◽  
Kaniz Fatima
Keyword(s):  
Transfer Learning ◽  
Data Augmentation ◽  
Intelligent Transportation System ◽  
Vehicle Classification ◽  
Improve Performance ◽  
Vehicle Type ◽  
Proposed Model ◽  
Vehicle Type Classification ◽  
Type Classification

Vehicle type classification plays an essential role in developing an intelligent transportation system (ITS). Based on the modern accomplishments of deep learning (DL) on image classification, we proposed a model based on transfer learning, incorporating data augmentation, for the recognition and classification of Bangladeshi native vehicle types. An extensive dataset of Bangladeshi native vehicles, encompassing 10,440 images, was developed. Here, the images are categorized into 13 common vehicle classes in Bangladesh. The method utilized was a residual network (ResNet-50)-based model, with extra classification blocks added to improve performance. Here, vehicle type features were automatically extracted and categorized. While conducting the analysis, a variety of metrics was used for the evaluation, including accuracy, precision, recall, and F1 − Score. In spite of the changing physical properties of the vehicles, the proposed model achieved progressive accuracy. Our proposed method surpasses the existing baseline method as well as two pre-trained DL approaches, AlexNet and VGG-16. Based on result comparisons, we have seen that, in the classification of Bangladeshi native vehicle types, our suggested ResNet-50 pre-trained model achieves an accuracy of 98.00%.

scholarly journals Pathological Analysis of Blood Cells Using Deep Learning Techniques

2020 ◽  
Vol 13 ◽  
Author(s):  
Virender Ranga ◽  
Shivam Gupta ◽  
Priyansh Agrawal ◽  
Jyoti Meena
Keyword(s):  
Deep Learning ◽  
Blood Cells ◽  
Well Being ◽  
Input Image ◽  
Medical Sciences ◽  
Human Errors ◽  
Deep Dive ◽  
Proposed Model ◽  
Better Than

Introduction: The major area of work of pathologists is concerned with detecting the diseases and helping the patients in their healthcare and well-being. The present method used by pathologists for this purpose is manually viewing the slides using a microscope and other instruments. But this method suffers from a lot of problems, like there is no standard way of diagnosing, human errors and it puts a heavy load on the laboratory men to diagnose such a large number of slides daily. Method: The slide viewing method is widely used and converted into digital form to produce high resolution images. This enables the area of deep learning and machine learning to deep dive into this field of medical sciences. In the present study, a neural based network has been proposed for classification of blood cells images into various categories. When input image is passed through the proposed architecture and all the hyper parameters and dropout ratio values are used in accordance with proposed algorithm, then model classifies the blood images with an accuracy of 95.24%. Result: After training the models on 20 epochs. The plots of training accuracy, testing accuracy and corresponding training loss, testing loss for proposed model is plotted using matplotlib and trends. Discussion: The performance of proposed model is better than existing standard architectures and other work done by various researchers. Thus, the proposed model enables the development of pathological system which will reduce human errors and daily load on laboratory men. This can also in turn help pathologists in carrying out their work more efficiently and effectively. Conclusion: In the present study, a neural based network has been proposed for classification of blood cells images into various categories. These categories have significance in the medical sciences. When input image is passed through the proposed architecture and all the hyper parameters and dropout ratio values are used in accordance with proposed algorithm, then model classifies the images with an accuracy of 95.24%. This accuracy is better than standard architectures.. Further it can be seen that the proposed neural network performs better than present related works carried by various researchers.

scholarly journals Classification of Indoor Human Fall Events Using Deep Learning

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 328
Author(s):  
Arifa Sultana ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba
Keyword(s):  
Physical Therapists ◽  
Binary Classification ◽  
Human Movement ◽  
Experimental Result ◽  
Video Frame ◽  
Human Beings ◽  
Video Frames ◽  
Alarm Systems ◽  
Proposed Model

Human fall identification can play a significant role in generating sensor based alarm systems, assisting physical therapists not only to reduce after fall effects but also to save human lives. Usually, elderly people suffer from various kinds of diseases and fall action is a very frequently occurring circumstance at this time for them. In this regard, this paper represents an architecture to classify fall events from others indoor natural activities of human beings. Video frame generator is applied to extract frame from video clips. Initially, a two dimensional convolutional neural network (2DCNN) model is proposed to extract features from video frames. Afterward, gated recurrent unit (GRU) network finds the temporal dependency of human movement. Binary cross-entropy loss function is calculated to update the attributes of the network like weights, learning rate to minimize the losses. Finally, sigmoid classifier is used for binary classification to detect human fall events. Experimental result shows that the proposed model obtains an accuracy of 99%, which outperforms other state-of-the-art models.

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

2020 ◽  
Vol 14 ◽  
Author(s):  
Lahari Tipirneni ◽  
Rizwan Patan
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Feature Descriptors ◽  
Histopathological Images ◽  
Viable Approach ◽  
Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

scholarly journals Classification of Post-Fire Responses of Woody Plants to include Pyrophobic Communities

Fire ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 15 ◽  
Author(s):  
Lynda D. Prior ◽  
David M. J. S. Bowman
Keyword(s):  
Woody Plants ◽  
Seed Banks ◽  
Classification Systems ◽  
Woody Vegetation ◽  
General Category ◽  
Conifer Species ◽  
Soil Seed Banks ◽  
Obligate Seeder ◽  
In Fire

Developing standardised classification of post-fire responses is essential for globally consistent comparisons of woody vegetation communities. Existing classification systems are based on responses of species growing in fire-prone environments. To accommodate species that occur in rarely burnt environments, we have suggested some important points of clarification to earlier schemes categorizing post-fire responses. We have illustrated this approach using several Australasian conifer species as examples of pyrophobic species. In particular, we suggest using the term “obligate seeder” for the general category of plants that rely on seed to reproduce, and qualifying this to “post-fire obligate seeder” for the narrower category of species with populations that recover from canopy fire only by seeding; the species are typically fire-cued, with large aerial or soil seed banks that germinate profusely following a fire, and grow and reproduce rapidly in order to renew the seed bank before the next fire.

scholarly journals Interobserver agreement of the Paris and simplified classifications of superficial colonic lesions: a Western study

2021 ◽  
Vol 09 (03) ◽  
pp. E388-E394
Author(s):  
Francesco Cocomazzi ◽  
Marco Gentile ◽  
Francesco Perri ◽  
Antonio Merla ◽  
Fabrizio Bossa ◽  
...  
Keyword(s):  
Interobserver Agreement ◽  
Classification Systems ◽  
Sensitivity Analyses ◽  
Size Estimation ◽  
Rater Agreement ◽  
Staff Members ◽  
Video Clips ◽  
Paris Classification ◽  
Level Of Agreement

Abstract Background and study aims The Paris classification of superficial colonic lesions has been widely adopted, but a simplified description that subgroups the shape into pedunculated, sessile/flat and depressed lesions has been proposed recently. The aim of this study was to evaluate the accuracy and inter-rater agreement among 13 Western endoscopists for the two classification systems. Methods Seventy video clips of superficial colonic lesions were classified according to the two classifications, and their size estimated. The interobserver agreement for each classification was assessed using both Cohen k and AC1 statistics. Accuracy was taken as the concordance between the standard morphology definition and that made by participants. Sensitivity analyses investigated agreement between trainees (T) and staff members (SM), simple or mixed lesions, distinct lesion phenotypes, and for laterally spreading tumors (LSTs). Results Overall, the interobserver agreement for the Paris classification was substantial (κ = 0.61; AC1 = 0.66), with 79.3 % accuracy. Between SM and T, the values were superimposable. For size estimation, the agreement was 0.48 by the κ-value, and 0.50 by AC1. For single or mixed lesions, κ-values were 0.60 and 0.43, respectively; corresponding AC1 values were 0.68 and 0.57. Evaluating the several different polyp subtypes separately, agreement differed significantly when analyzed by the k-statistics (0.08–0.12) or the AC1 statistics (0.59–0.71). Analyses of LSTs provided a κ-value of 0.50 and an AC1 score of 0.62, with 77.6 % accuracy. The simplified classification outperformed the Paris classification: κ = 0.68, AC1 = 0.82, accuracy = 91.6 %. Conclusions Agreement is often measured with Cohen’s κ, but we documented higher levels of agreement when analyzed with the AC1 statistic. The level of agreement was substantial for the Paris classification, and almost perfect for the simplified system.

Hierarchical Scheme for Vehicle Make and Model Recognition

2021 ◽  
pp. 036119812110197
Author(s):  
Chaoqing Wang ◽  
Junlong Cheng ◽  
Yuefei Wang ◽  
Yurong Qian
Keyword(s):  
Intelligent Transportation Systems ◽  
Recognition Accuracy ◽  
Transportation Systems ◽  
Feature Descriptor ◽  
Vehicle Recognition ◽  
Public Dataset ◽  
Classification Of Images ◽  
Model Recognition ◽  
Hierarchical Scheme

A vehicle make and model recognition (VMMR) system is a common requirement in the field of intelligent transportation systems (ITS). However, it is a challenging task because of the subtle differences between vehicle categories. In this paper, we propose a hierarchical scheme for VMMR. Specifically, the scheme consists of (1) a feature extraction framework called weighted mask hierarchical bilinear pooling (WMHBP) based on hierarchical bilinear pooling (HBP) which weakens the influence of invalid background regions by generating a weighted mask while extracting features from discriminative regions to form a more robust feature descriptor; (2) a hierarchical loss function that can learn the appearance differences between vehicle brands, and enhance vehicle recognition accuracy; (3) collection of vehicle images from the Internet and classification of images with hierarchical labels to augment data for solving the problem of insufficient data and low picture resolution and improving the model’s generalization ability and robustness. We evaluate the proposed framework for accuracy and real-time performance and the experiment results indicate a recognition accuracy of 95.1% and an FPS (frames per second) of 107 for the framework for the Stanford Cars public dataset, which demonstrates the superiority of the method and its availability for ITS.

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