scholarly journals MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection

2018 ◽  
Author(s):  
Wenchi Ma ◽  
Yuanwei Wu ◽  
Zongbo Wang ◽  
Guanghui Wang
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Multi Scale

scholarly journals Multi-scale target detection based on morphological shared-weight neural network

2017 ◽  
Author(s):  
◽  
Shuxian Shen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Convolutional Neural Networks ◽  
Network Structure ◽  
Training Data ◽  
Multi Scale ◽  
Neural Network Structure

Convolutional Neural Networks (CNN) are a popular neural network structure for image based applications. This thesis discusses an alternative network, the morphological shared-weight neural network (MSNN) for object detection. In this thesis, three combined network structures are developed for multi-scale object detection. The dataset used for the experiments presented here were created by the author for this thesis study. The convolutional neural network is used as the baseline for judging the performance of the MSNN. Experiments suggest that when training data is limited, the MSNN has a more robust and precise performance as compared with the CNN.

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.

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