Real Time Object Identification Using Neural Network with Caffe Model

2019 ◽  
Vol 7 (5) ◽  
pp. 175-182
Author(s):  
Anjali Nema ◽  
Anshul Khurana
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Object Identification

scholarly journals Object Identification and Localization Using Grad-CAM++ with Mask Regional Convolution Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1541
Author(s):  
Xavier Alphonse Inbaraj ◽  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Semantic Segmentation ◽  
Object Identification ◽  
Convolution Neural Network ◽  
Object Localization ◽  
Localization Method ◽  
Wide Range ◽  
Detection Of Objects ◽  
Real Time Applications

One of the fundamental advancements in the deployment of object detectors in real-time applications is to improve object recognition against obstruction, obscurity, and noises in images. In addition, object detection is a challenging task since it needs the correct detection of objects from images. Semantic segmentation and localization are an important module to recognizing an object in an image. The object localization method (Grad-CAM++) is mostly used by researchers for object localization, which uses the gradient with a convolution layer to build a localization map for important regions on the image. This paper proposes a method called Combined Grad-CAM++ with the Mask Regional Convolution Neural Network (GC-MRCNN) in order to detect objects in the image and also localization. The major advantage of proposed method is that they outperform all the counterpart methods in the domain and can also be used in unsupervised environments. The proposed detector based on GC-MRCNN provides a robust and feasible ability in detecting and classifying objects exist and their shapes in real time. It is found that the proposed method is able to perform highly effectively and efficiently in a wide range of images and provides higher resolution visual representation than existing methods (Grad-CAM, Grad-CAM++), which was proven by comparing various algorithms.

scholarly journals Object Identification and Localization using Convolution Neural Network

2021 ◽  
pp. 26-32
Author(s):  
Bontle Goitsemedi ◽  
Kedibonye Keletso
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Object Identification ◽  
Convolution Neural Network ◽  
Drop Out ◽  
Wide Scope ◽  
Real Time Applications

Improving object identification against impediment, obscure and clamor image is a basic advance to deploy detector in real time applications. Since it is preposterous to expect to debilitate all picture abandons through information assortment, numerous specialists look to produce hard examples in preparing. The produced hard examples are either pictures or highlight maps with coarse patches exited in the spatial measurements. Huge overheads are needed in preparing the extra hard examples and additionally assessing drop-out patches utilizing additional organization branches. In this paper we proposed GRAD CAM++ with Mask Regional Convolution Neural Network (Mask RCNN) based item limitation and identification. The significant advantages of utilizing Mask R-CNN is that they beat all the partner techniques in the space and can likewise be utilized in unaided environments. The proposed identifier dependent on GRAD CAM++ with Mask R-CNN gives a vigorous and plausible capacity on recognizing and grouping objects exist and their shapes progressively on location. It is discovered that the proposed strategy can perform exceptionally successful and productive in a wide scope of pictures and gives higher goal visual portrayal.

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.

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.

Neural network for real-time synthesis of FIR filters

1989 ◽  
Vol 25 (17) ◽  
pp. 1199 ◽  
Author(s):  
G. Martinelli ◽  
R. Perfetti
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Fir Filters
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