Open AccessObject detection method on station logo with single shot multi‐box detector
Author(s) -
Rong Fei,
Shasha Li,
Qingzheng Xu,
Kun Liu
Publication year - 2020
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.1213
Subject(s) - computer science , artificial intelligence , frame (networking) , sample (material) , object detection , computer vision , detector , filter (signal processing) , convolutional neural network , object (grammar) , shot (pellet) , process (computing) , position (finance) , artificial neural network , pattern recognition (psychology) , telecommunications , chemistry , finance , chromatography , economics , operating system , organic chemistry
In this work, the authors design an object detection method by the characteristics of the station with convolutional neural network, such as small scale‐to‐height ratio change and relatively fixed position. In order to realise the pre‐processing and feature extraction of the station data, they collect the video samples and filter, frame, label and process to these samples. Also then, the training sample data and the test sample data are divided proportionally to train the station detection model. After that, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments proved its validity.