Application of Transfer Learning for Object Recognition Using Convolutional Neural Networks

The global development and progress in scientific paraphernalia and technology is the fundamental reason for the rapid increasein the data volume. Several significant techniques have been introducedfor image processing and object detection owing to this advancement. The promising features and transfer learning of ConvolutionalNeural Network (CNN) havegained much attention around the globe by researchers as well as computer vision society, as a result of which, several remarkable breakthroughs were achieved. This paper comprehensively reviews the data classification, history as well as architecture of CNN and well-known techniques bytheir boons and absurdities. Finally, a discussion for implementation of CNN over object detection for effectual results based on their critical analysis and performances is presented

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Interpretation of Swedish Sign Language Using Convolutional Neural Networks and Transfer Learning

SN Computer Science ◽

10.1007/s42979-021-00612-w ◽

2021 ◽

Vol 2 (3) ◽

Author(s):

Gustaf Halvardsson ◽

Johanna Peterson ◽

César Soto-Valero ◽

Benoit Baudry

Keyword(s):

Neural Networks ◽

Sign Language ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Web Application ◽

Training Dataset ◽

Motion Processing ◽

Image Perception ◽

Sign Languages ◽

High Level

AbstractThe automatic interpretation of sign languages is a challenging task, as it requires the usage of high-level vision and high-level motion processing systems for providing accurate image perception. In this paper, we use Convolutional Neural Networks (CNNs) and transfer learning to make computers able to interpret signs of the Swedish Sign Language (SSL) hand alphabet. Our model consists of the implementation of a pre-trained InceptionV3 network, and the usage of the mini-batch gradient descent optimization algorithm. We rely on transfer learning during the pre-training of the model and its data. The final accuracy of the model, based on 8 study subjects and 9400 images, is 85%. Our results indicate that the usage of CNNs is a promising approach to interpret sign languages, and transfer learning can be used to achieve high testing accuracy despite using a small training dataset. Furthermore, we describe the implementation details of our model to interpret signs as a user-friendly web application.

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Algorithm Selection Framework for Legalization Using Deep Convolutional Neural Networks and Transfer Learning

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems ◽

10.1109/tcad.2021.3079126 ◽

2021 ◽

pp. 1-1

Author(s):

Renan Netto ◽

Sheiny Fabre ◽

Tiago Augusto Fontana ◽

Vinicius Livramento ◽

Laercio L. Pilla ◽

...

Keyword(s):

Neural Networks ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Deep Convolutional Neural Networks ◽

Algorithm Selection ◽

Selection Framework

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Grape Leaf Multi-disease Detection with Confidence Value Using Transfer Learning Integrated to Regions with Convolutional Neural Networks

2020 IEEE REGION 10 CONFERENCE (TENCON) ◽

10.1109/tencon50793.2020.9293866 ◽

2020 ◽

Author(s):

Sandy Lauguico ◽

Ronnie Concepcion ◽

Rogelio Ruzcko Tobias ◽

Argel Bandala ◽

Ryan Rhay Vicerra ◽

...

Keyword(s):

Neural Networks ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Disease Detection ◽

Grape Leaf ◽

Confidence Value

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Human and bird detection and classification based on Doppler radar spectrograms and vision images using convolutional neural networks

International Journal of Advanced Robotic Systems ◽

10.1177/17298814211010569 ◽

2021 ◽

Vol 18 (3) ◽

pp. 172988142110105

Author(s):

Jnana Sai Abhishek Varma Gokaraju ◽

Weon Keun Song ◽

Min-Ho Ka ◽

Somyot Kaitwanidvilai

Keyword(s):

Neural Networks ◽

Object Recognition ◽

Convolutional Neural Networks ◽

Doppler Radar ◽

Autonomous Vehicle ◽

Sampling Point ◽

Body Segment ◽

Kinematic Modeling ◽

Deep Convolutional Neural Networks ◽

Kinematic Models

The study investigated object detection and classification based on both Doppler radar spectrograms and vision images using two deep convolutional neural networks. The kinematic models for a walking human and a bird flapping its wings were incorporated into MATLAB simulations to create data sets. The dynamic simulator identified the final position of each ellipsoidal body segment taking its rotational motion into consideration in addition to its bulk motion at each sampling point to describe its specific motion naturally. The total motion induced a micro-Doppler effect and created a micro-Doppler signature that varied in response to changes in the input parameters, such as varying body segment size, velocity, and radar location. Micro-Doppler signature identification of the radar signals returned from the target objects that were animated by the simulator required kinematic modeling based on a short-time Fourier transform analysis of the signals. Both You Only Look Once V3 and Inception V3 were used for the detection and classification of the objects with different red, green, blue colors on black or white backgrounds. The results suggested that clear micro-Doppler signature image-based object recognition could be achieved in low-visibility conditions. This feasibility study demonstrated the application possibility of Doppler radar to autonomous vehicle driving as a backup sensor for cameras in darkness. In this study, the first successful attempt of animated kinematic models and their synchronized radar spectrograms to object recognition was made.

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