scholarly journals Real Time Object Detection using CNN

2018 ◽  
Vol 7 (2.24) ◽  
pp. 33
Author(s):  
Akash Tripathi ◽  
T V. Ajay Kumar ◽  
Tarun Kanth Dhansetty ◽  
J Selva Kumar
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Image Classification ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Small Sample ◽  
Small Data ◽  
Processing Unit ◽  
Sift Algorithm ◽  
Central Processing

Achieving new heights in object detection and image classification was made possible because of Convolution Neural Network(CNN). However, compared to image classification the object detection tasks are more difficult to analyze, more energy consuming and computation intensive. To overcome these challenges, a novel approach is developed for real time object detection applications to improve the accuracy and energy efficiency of the detection process. This is achieved by integrating the Convolutional Neural Networks (CNN) with the Scale Invariant Feature Transform (SIFT) algorithm. Here, we obtain high accuracy output with small sample data to train the model by integrating the CNN and SIFT features. The proposed detection model is a cluster of multiple deep convolutional neural networks and hybrid CNN-SIFT algorithm. The reason to use the SIFT featureis to amplify the model‟s capacity to detect small data or features as the SIFT requires small datasets to detect objects. Our simulation results show better performance in accuracy when compared with the conventional CNN method. As the resources like RAM, graphic card, ROM, etc. are limited we propose a pipelined implementation on an aggregate Central Processing Unit(CPU) and Graphical Processing Unit(GPU) platform.  

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

scholarly journals Data Augmentation Methods Applying Grayscale Images for Convolutional Neural Networks in Machine Vision

2021 ◽  
Vol 11 (15) ◽  
pp. 6721
Author(s):  
Jinyeong Wang ◽  
Sanghwan Lee
Keyword(s):  
Neural Networks ◽  
Machine Vision ◽  
Object Detection ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Image Data ◽  
Manufacturing Productivity ◽  
Smart Factories ◽  
Grayscale Images

In increasing manufacturing productivity with automated surface inspection in smart factories, the demand for machine vision is rising. Recently, convolutional neural networks (CNNs) have demonstrated outstanding performance and solved many problems in the field of computer vision. With that, many machine vision systems adopt CNNs to surface defect inspection. In this study, we developed an effective data augmentation method for grayscale images in CNN-based machine vision with mono cameras. Our method can apply to grayscale industrial images, and we demonstrated outstanding performance in the image classification and the object detection tasks. The main contributions of this study are as follows: (1) We propose a data augmentation method that can be performed when training CNNs with industrial images taken with mono cameras. (2) We demonstrate that image classification or object detection performance is better when training with the industrial image data augmented by the proposed method. Through the proposed method, many machine-vision-related problems using mono cameras can be effectively solved by using CNNs.

scholarly journals Object detection in real time based on improved single shot multi-box detector algorithm

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Ashwani Kumar ◽  
Zuopeng Justin Zhang ◽  
Hongbo Lyu
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Single Layer ◽  
Single Shot ◽  
Convolutional Network ◽  
Detection Techniques ◽  
Computational Performance ◽  
Speed Up

Abstract In today’s scenario, the fastest algorithm which uses a single layer of convolutional network to detect the objects from the image is single shot multi-box detector (SSD) algorithm. This paper studies object detection techniques to detect objects in real time on any device running the proposed model in any environment. In this paper, we have increased the classification accuracy of detecting objects by improving the SSD algorithm while keeping the speed constant. These improvements have been done in their convolutional layers, by using depth-wise separable convolution along with spatial separable convolutions generally called multilayer convolutional neural networks. The proposed method uses these multilayer convolutional neural networks to develop a system model which consists of multilayers to classify the given objects into any of the defined classes. The schemes then use multiple images and detect the objects from these images, labeling them with their respective class label. To speed up the computational performance, the proposed algorithm is applied along with the multilayer convolutional neural network which uses a larger number of default boxes and results in more accurate detection. The accuracy in detecting the objects is checked by different parameters such as loss function, frames per second (FPS), mean average precision (mAP), and aspect ratio. Experimental results confirm that our proposed improved SSD algorithm has high accuracy.

Real-Time Object Detection for Millimeter-Wave Images Based on Improved Faster Regions with Convolutional Neural Networks

2019 ◽  
Vol 56 (13) ◽  
pp. 131009
Author(s):  
侯冰基 Bingji Hou ◽  
杨明辉 Minghui Yang ◽  
孙晓玮 Xiaowei Sun
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Real Time ◽  
Millimeter Wave ◽  
Convolutional Neural Networks

scholarly journals Assessing Wear Out of Tyre using Opencv & Convolutional Neural Networks

2021 ◽  
Vol 2089 (1) ◽  
pp. 012001
Author(s):  
C Kishor Kumar Reddy ◽  
P R Anisha ◽  
R Madana Mohana
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Wear Testing ◽  
Timely Manner ◽  
Safe Driving ◽  
The Road ◽  
Tire Wear ◽  
Wear And Tear

Abstract This work proposes a process to detect the wear and tear of car tires. Tire is the only part of the road that does not interact with the road. The condition of the wheel should therefore be monitored in a timely manner for safe driving. Tired fatigue occurs due to limitations such as that the tread limit is less than 1.6 cm, the damage to the rubber, where there are pipes around 4 to 5, the affected tire. We look at some of the above limitations of tire wear testing using computer viewing techniques such as opencv and convolutional neural networks. Opencv and convolutional neural networks are widely used for object detection and image classification. We used these methods and obtained 90.90% accuracy, with which we can predict tire wear to avoid dangerous accidents..

scholarly journals Object Detection and Identification

2021 ◽  
Vol 10 (3) ◽  
pp. 1611-1618
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Text To Speech ◽  
Final Model ◽  
Detection Systems ◽  
Detection And Identification ◽  
Main Motive ◽  
Computer Based

Object Detection systems have been growing in the last few years for various applications. Since the hardware can not detect the smallest objects. Many algorithms are used for object detection like Yolo, R-CNN, Fast R-CNN, Faster R-CNN, etc. object detection using YOLO is faster than other algorithms and the YOLO scans the whole image completely at one time. Object detection, which is based on Convolutional Neural Networks (CNNs) and it's based on classification and localization. An object is detected by extracting the features of an object like the color of the object, the texture of the object or shape, or some other features. Then based on these features, objects are classified into many classes and each class is assigned a label. When we subsequently provide an image to the model, it will output many objects it detects, the location of a bounding box that contains every object with their label and score indicates the confidence. Text-To-Speech (TTS) conversion is a computer-based systemthat requires for the label are converted text-to-speech. The main motive is that the smallest amount of objects can be detected object and labeling the object with voice for real-time object detection. The final model architecture proposed is more accurate and provides the fast result of object detection with voice as compared to previous researches

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