Efficient, Low-Cost, Real-Time Video Super-Resolution Network

2021 ◽  
pp. 200-211
Author(s):  
Guanqun Liu ◽  
Xin Wang ◽  
Daren Zha ◽  
Lei Wang ◽  
Lin Zhao
Keyword(s):  
Low Cost ◽  
Author(s):  
Donya Khaledyan ◽  
Abdolah Amirany ◽  
Kian Jafari ◽  
Mohammad Hossein Moaiyeri ◽  
Abolfazl Zargari Khuzani ◽  
...  

2003 ◽  
Author(s):  
Gustavo M. Callico ◽  
Rafael Peset Llopis ◽  
Antonio Nunez ◽  
Ramanathan Sethuraman

Author(s):  
Gabriel de Almeida Souza ◽  
Larissa Barbosa ◽  
Glênio Ramalho ◽  
Alexandre Zuquete Guarato

2007 ◽  
Author(s):  
R. E. Crosbie ◽  
J. J. Zenor ◽  
R. Bednar ◽  
D. Word ◽  
N. G. Hingorani

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yong He ◽  
Hong Zeng ◽  
Yangyang Fan ◽  
Shuaisheng Ji ◽  
Jianjian Wu

In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).


2012 ◽  
Author(s):  
Douglas R. Droege ◽  
Russell C. Hardie ◽  
Brian S. Allen ◽  
Alexander J. Dapore ◽  
Jon C. Blevins

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