Next Article in Journal
An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN
Previous Article in Journal
Doppler Shift Time Expansion Resolution and Spectral Performance in Wideband Real-Time RF Channel Emulators
Previous Article in Special Issue
A TAM-Based Study of the Attitude towards Use Intention of Multimedia among School Teachers
Open AccessArticle

Motion Control System of Unmanned Railcars Based on Image Recognition

Department of Electrical Engineering, I-Shou University, Kaohsiung City 84001, Taiwan
Author to whom correspondence should be addressed.
Appl. Syst. Innov. 2019, 2(1), 9;
Received: 31 January 2019 / Revised: 20 February 2019 / Accepted: 26 February 2019 / Published: 5 March 2019
(This article belongs to the Special Issue Selected Papers from IEEE ICICE 2018)
The main purpose of this paper is to construct an autopilot system for unmanned railcars based on computer vision technology in a fixed luminous environment. Four graphic predefined signs of different colors and shapes serve as motion commands of acceleration, deceleration, reverse and stop for the motion control system of railcars based on image recognition. The predefined signs’ strong classifiers were trained based on Haar-like feature training and AdaBoosting from Open Source Computer Vision Library (OpenCV). Comprehensive system integrations such as hardware, device drives, protocols, an application program in Python and man machine interface have been properly done. The objectives of this research include: (1) Verifying the feasibility of graphic predefined signs serving as commands of a motion control system of railcars with computer vision through experiments; (2) Providing reliable solutions for motion control of unmanned railcars, based on image recognition at affordable cost. The experiment results successfully verify the proposed methodology and integrated system. In the main program, every predefined sign must be detected at least three times in consecutive images within 0.2 s before the system confirms the detection. This digital filter like feature can filter out false detections and make the correct rate of detections close to 100%. After detecting a predefined sign, it was observed that the system could generate new motion commands to drive the railcars within 0.3 s. Therefore, both real time performance and the precision of the system are good. Since the sensing and control devices of the proposed system consist of computer, camera and predefined signs only, both the implementation and maintenance costs are very low. In addition, the proposed system is immune to electromagnetic interference, so it is ideal to merge into popular radio Communication Based Train Control (CBTC) systems in railways to improve the safety of operations. View Full-Text
Keywords: image recognition; computer vision; OpenCV; AdaBoosting; motion control image recognition; computer vision; OpenCV; AdaBoosting; motion control
Show Figures

Figure 1

MDPI and ACS Style

Tseng, Y.-W.; Hung, T.-W.; Pan, C.-L.; Wu, R.-C. Motion Control System of Unmanned Railcars Based on Image Recognition. Appl. Syst. Innov. 2019, 2, 9.

AMA Style

Tseng Y-W, Hung T-W, Pan C-L, Wu R-C. Motion Control System of Unmanned Railcars Based on Image Recognition. Applied System Innovation. 2019; 2(1):9.

Chicago/Turabian Style

Tseng, Yuan-Wei; Hung, Tsung-Wui; Pan, Chung-Long; Wu, Rong-Ching. 2019. "Motion Control System of Unmanned Railcars Based on Image Recognition" Appl. Syst. Innov. 2, no. 1: 9.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop