scholarly journals Comparison Of Cellular Video Quality For Object Detection Using Neural Network Convolution

SinkrOn ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 260 ◽  
Author(s):  
Kevin Kevin ◽  
Nico Gunawan ◽  
Mariana Erfan Kristiani Zagoto ◽  
Laurentius Laurentius ◽  
Amir Mahmud Husein
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Real Time ◽  
Video Quality ◽  
Vehicle Detection ◽  
Neural Network Method ◽  
Network Method ◽  
Object Of Study ◽  
Capture Capacity ◽  
Detection And Recognition

Abstract— The purpose of this study is to compare the video quality between the Samsung HP camera and the Xiaomi HP camera. The object of study was UNPRI students who walked through the front yard of the UNPRI SEKIP campus. Here we test how accurate the camera's HP capture capacity is used to take the video. The method used to test this research is the Convolution Neural Network method. Object detection and recognition aim to detect and classify objects that can be applied to various fields such as face, human, pedestrian, vehicle detection (Pedoeem & Huang, 2018), besides the ability to find, identify, track and stabilize objects in various poses and important backgrounds in many real-time video applications. Object detection, tracking, alignment and stabilization have become very interesting fields of research in the vision and recognition of computer patterns due to the challenging nature of several slightly different objects such as object detection, where the algorithm must be precise enough to identify, track and center an object from the others

Real-time Tissue Thermometry Using an Acoustic Neural Network Method during HIFU Treatment

2010 ◽  
Author(s):  
Liexiang Fan ◽  
K. Michael Sekins ◽  
Kullervo Hynynen ◽  
Jacques Souquet
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Hifu Treatment ◽  
Neural Network Method ◽  
Network Method

scholarly journals An Efficient Deep Convolutional Neural Network Approach for Object Detection and Recognition Using a Multi-Scale Anchor Box in Real-Time

2021 ◽  
Vol 13 (12) ◽  
pp. 307
Author(s):  
Vijayakumar Varadarajan ◽  
Dweepna Garg ◽  
Ketan Kotecha
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Real Time ◽  
Semantic Segmentation ◽  
Object Identification ◽  
Detection Accuracy ◽  
Neural Network Approach ◽  
Multi Scale ◽  
Scale Anchor ◽  
Detection And Recognition

Deep learning is a relatively new branch of machine learning in which computers are taught to recognize patterns in massive volumes of data. It primarily describes learning at various levels of representation, which aids in understanding data that includes text, voice, and visuals. Convolutional neural networks have been used to solve challenges in computer vision, including object identification, image classification, semantic segmentation and a lot more. Object detection in videos involves confirming the presence of the object in the image or video and then locating it accurately for recognition. In the video, modelling techniques suffer from high computation and memory costs, which may decrease performance measures such as accuracy and efficiency to identify the object accurately in real-time. The current object detection technique based on a deep convolution neural network requires executing multilevel convolution and pooling operations on the entire image to extract deep semantic properties from it. For large objects, detection models can provide superior results; however, those models fail to detect the varying size of the objects that have low resolution and are greatly influenced by noise because the features after the repeated convolution operations of existing models do not fully represent the essential characteristics of the objects in real-time. With the help of a multi-scale anchor box, the proposed approach reported in this paper enhances the detection accuracy by extracting features at multiple convolution levels of the object. The major contribution of this paper is to design a model to understand better the parameters and the hyper-parameters which affect the detection and the recognition of objects of varying sizes and shapes, and to achieve real-time object detection and recognition speeds by improving accuracy. The proposed model has achieved 84.49 mAP on the test set of the Pascal VOC-2007 dataset at 11 FPS, which is comparatively better than other real-time object detection models.

Methods for evaluation the manufacturability of a vehicle

2020 ◽  
pp. 152-160
Keyword(s):  
Neural Network ◽  
Initial Data ◽  
Comparative Data ◽  
Training Algorithm ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Calculation Results ◽  
Production And Operation

Methods for evaluation the manufacturability of a vehicle in the field of production and operation based on an energy indicator, expert estimates and usage of a neural network are stated. By using the neural network method the manufacturability of a car in a complex and for individual units is considered. The preparation of the initial data at usage a neural network for predicting the manufacturability of a vehicle is shown; the training algorithm and the architecture for calculating the manufacturability of the main units are given. According to the calculation results, comparative data on the manufacturability vehicles of various brands are given.

Sign in / Sign up

Export Citation Format

Share Document