scholarly journals LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems

2017 ◽  
Author(s):  
Subarna Tripathi ◽  
Gokce Dane ◽  
Byeongkeun Kang ◽  
Vasudev Bhaskaran ◽  
Truong Nguyen
Keyword(s):  
Neural Networks ◽  
Embedded Systems ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Low Complexity ◽  
Fully Convolutional Neural Networks

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 Fully convolutional neural networks for polyp segmentation in colonoscopy

2017 ◽  
Author(s):  
Patrick Brandao ◽  
Evangelos Mazomenos ◽  
Gastone Ciuti ◽  
Renato Caliò ◽  
Federico Bianchi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fully Convolutional Neural Networks
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