Abstract
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.
Highlights
In many automation applications, for example, sushi trains in restaurants, automatic manufacture lines, warehouse storages and Mass Rapid Transit (MRT), unmanned self-propelled railcars play many important roles
Considering that many railcars operate in fixed luminous environments, we developed a motion control system of unmanned railcars based on image recognition in this research
Histogram equalization is applied in the main program to normalize the brightness and increase the contrast of the images, when the railcar operates in brightness-varying outdoor surroundings, to better improve the correct rate we should train several strong classifiers in different conditions of ambient brightness
Summary
For example, sushi trains in restaurants, automatic manufacture lines, warehouse storages and Mass Rapid Transit (MRT), unmanned self-propelled railcars play many important roles. Many railcars operate in surroundings of fixed ambient brightness, such as indoor or subways. Unmanned railcars should be able to control their own motions in response to different situations automatically. Due to their functional similarity, the developing control systems for unmanned railcars can borrow some distinguishing features from more complicated train control systems in a railway. We first briefly review the evolution of the train control system. The train control system in a railway is a signaling system. If a train can correctly identify the signals received from tracksides, it can operate safely
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