Coal dust image recognition based on improved VGG convolution network

Dongyan Li; Zheng Wang; Helin Zhang

doi:10.1117/12.2576974

12 October 2020 Coal dust image recognition based on improved VGG convolution network

Dongyan Li, Zheng Wang, Helin Zhang

Proceedings Volume 11574, International Symposium on Artificial Intelligence and Robotics 2020; 115740O (2020) https://doi.org/10.1117/12.2576974
Event: International Symposium on Artificial Intelligence and Robotics (ISAIR), 2020, Kitakyushu, Japan

Abstract

In views of the problems of particle contour overlap and unclear texture detection of coal dust explosion, this paper proposed a method based on improved deep learning vgg-16 convolutional neural network model to obtain the feature information of particle image. Based on the vgg-16 network model, the SELayer is added after sampling under the first two convolutional layers to compress and extract the deep features of the particle image. The original SoftMax classifier was replaced by a binary classifier to optimize the model parameter structure. The weight parameters of convolution layer and pooling layer in the pre-training model were shared by micro-migration learning to speed up the operation. Samples were randomly selected from the constructed coal dust image as training set and test set to test the performance indexes of the model. The experimental results show that the proposed method has 2% promoted of recognition accuracy to the conventional methods, and achieved a lower loss value, which can meet the detection requirements of coal dust particle image.

Citation Download Citation

Dongyan Li, Zheng Wang, and Helin Zhang "Coal dust image recognition based on improved VGG convolution network", Proc. SPIE 11574, International Symposium on Artificial Intelligence and Robotics 2020, 115740O (12 October 2020); https://doi.org/10.1117/12.2576974

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available