Rotation Symmetry Object Classification Using Structure Constrained Convolutional Neural Network

2018 ◽  
pp. 139-146
Author(s):  
Seunghwa Yu ◽  
Seugnkyu Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Object Classification ◽  
Rotation Symmetry

Deep Convolutional Neural Network for Object Classification

2021 ◽  
pp. 317-343
Author(s):  
Amira Ahmad Al-Sharkawy ◽  
Gehan A. Bahgat ◽  
Elsayed E. Hemayed ◽  
Samia Abdel-Razik Mashali
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Computational Models ◽  
Human Performance ◽  
Deep Neural Networks ◽  
Object Classification ◽  
Classification Problem ◽  
Deep Convolutional Neural Network ◽  
Object Appearance

Object classification problem is essential in many applications nowadays. Human can easily classify objects in unconstrained environments easily. Classical classification techniques were far away from human performance. Thus, researchers try to mimic the human visual system till they reached the deep neural networks. This chapter gives a review and analysis in the field of the deep convolutional neural network usage in object classification under constrained and unconstrained environment. The chapter gives a brief review on the classical techniques of object classification and the development of bio-inspired computational models from neuroscience till the creation of deep neural networks. A review is given on the constrained environment issues: the hardware computing resources and memory, the object appearance and background, and the training and processing time. Datasets that are used to test the performance are analyzed according to the images environmental conditions, besides the dataset biasing is discussed.

Object Classification Based on Multitask Convolutional Neural Network

2019 ◽  
Vol 56 (23) ◽  
pp. 231502 ◽  
Author(s):  
张苗辉 Zhang Miaohui ◽  
张博 Zhang Bo ◽  
高诚诚 Gao Chengcheng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Object Classification

scholarly journals Deep Learning Object Detector Using a Combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and Classic Object Detection Algorithm

2020 ◽  
Vol 28 (S2) ◽  
Author(s):  
Asmida Ismail ◽  
Siti Anom Ahmad ◽  
Azura Che Soh ◽  
Mohd Khair Hassan ◽  
Hazreen Haizi Harith
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Object Classification ◽  
Detection Algorithm ◽  
Learning Object ◽  
Sliding Windows ◽  
Detection Algorithms

The object detection system is a computer technology related to image processing and computer vision that detects instances of semantic objects of a certain class in digital images and videos. The system consists of two main processes, which are classification and detection. Once an object instance has been classified and detected, it is possible to obtain further information, including recognizes the specific instance, track the object over an image sequence and extract further information about the object and the scene. This paper presented an analysis performance of deep learning object detector by combining a deep learning Convolutional Neural Network (CNN) for object classification and applies classic object detection algorithms to devise our own deep learning object detector. MiniVGGNet is an architecture network used to train an object classification, and the data used for this purpose was collected from specific indoor environment building. For object detection, sliding windows and image pyramids were used to localize and detect objects at different locations, and non-maxima suppression (NMS) was used to obtain the final bounding box to localize the object location. Based on the experiment result, the percentage of classification accuracy of the network is 80% to 90% and the time for the system to detect the object is less than 15sec/frame. Experimental results show that there are reasonable and efficient to combine classic object detection method with a deep learning classification approach. The performance of this method can work in some specific use cases and effectively solving the problem of the inaccurate classification and detection of typical features.

scholarly journals ROBTK: An intelligent robot to transport objects

2019 ◽  
Author(s):  
Wallyson O. Das Mecês ◽  
Edson M. Da Costa ◽  
Jailton W. Tavares ◽  
Patrícia P. Diniz ◽  
Renato H. Torres
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Medical Care ◽  
Object Classification ◽  
Intelligent Robot ◽  
Hours Worked ◽  
Robot Intelligence ◽  
Human Labor ◽  
Work Accident

Studies show that every year the number of accidents and medical care expenses increase due to the work accident. In the context of transporting objects, many activities are still carried out by human labor which in many cases may be subject to disasters due to the high number of hours worked or due to unhealthy activities. Taking into account the presented scenario, this work aims to introduce the robot ROBTK an intelligent robot to transport objects. We developed robot intelligence building a convolutional neural network (CNN) to perform object classification. The tests performed showed that both the intelligence and the mechanics designed are efficient.

Three-dimensional convolutional neural network–based underground object classification using three-dimensional ground penetrating radar data

2020 ◽  
Vol 19 (6) ◽  
pp. 1884-1893
Author(s):  
Shekhroz Khudoyarov ◽  
Namgyu Kim ◽  
Jong-Jae Lee
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Ground Penetrating Radar ◽  
Three Dimensional ◽  
Object Classification ◽  
Radar Data ◽  
Two Dimensional ◽  
Underground Cavities ◽  
Ground Penetrating

Ground-penetrating radar is a typical sensor system for analyzing underground facilities such as pipelines and rebars. The technique also can be used to detect an underground cavity, which is a potential sign of urban sinkholes. Multichannel ground-penetrating radar devices are widely used to detect underground cavities thanks to the capacity of informative three-dimensional data. Nevertheless, the three-dimensional ground-penetrating radar data interpretation is unclear and complicated when recognizing underground cavities because similar ground-penetrating radar data reflected from different underground objects are often mixed with the cavities. As it is prevalently known that the deep learning algorithm-based techniques are powerful at image classification, deep learning-based techniques for underground object detection techniques using two-dimensional GPR (ground-penetrating radar) radargrams have been researched upon in recent years. However, spatial information of underground objects can be characterized better in three-dimensional ground-penetrating radar voxel data than in two-dimensional ground-penetrating radar images. Therefore, in this study, a novel underground object classification technique is proposed by applying deep three-dimensional convolutional neural network on three-dimensional ground-penetrating radar data. First, a deep convolutional neural network architecture was developed using three-dimensional convolutional networks for recognizing spatial underground objects such as, pipe, cavity, manhole, and subsoil. The framework of applying the three-dimensional convolutional neural network into three-dimensional ground-penetrating radar data was then proposed and experimentally validated using real three-dimensional ground-penetrating radar data. In order to do that, three-dimensional ground-penetrating radar block data were used to train the developed three-dimensional convolutional neural network and to classify unclassified three-dimensional ground-penetrating radar data collected from urban roads in Seoul, South Korea. The validation results revealed that four underground objects (pipe, cavity, manhole, and subsoil) are successfully classified, and the average classification accuracy was 97%. In addition, a false alarm was rarely indicated.

scholarly journals Arbitrary size ratio canonical object classification based on Convolutional Neural Network

2019 ◽  
Vol 147 ◽  
pp. 102-108 ◽  
Author(s):  
Wenlin Zhang ◽  
Ping Zhou ◽  
Sheng Li ◽  
Hua Yan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Object Classification ◽  
Size Ratio ◽  
Arbitrary Size
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