Automated Detection of Hazardous Areas in Railway Stabling Depots Utilizing Passive Infrared Motion Sensors

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Chrzthemmarcdickins B. Bacat John Bryan L. Bulan Jan Marty A. Cantero Ivann Dexter C. Lampano Connie C. Aunario Carlo Jay G. Delizo Sherlie D. Bunag Venerando A. Sison Julius G. Garcia

Abstract

This research project aims to construct a motion sensor indication of hazardous railway depot areas to improve the safety of the crew. The project proposes a localized, solar-powered safety system that will govern the inherent dangers of railway station operations and avert possible accidents. The focus was developing and building a prototype incorporating motion sensor technology that uses visual and audio alerts to alert staff of impending dangers in real-time. This method would therefore design a system that consisted of a Passive Infrared (PIR) motion sensor, a siren alarm, a Light Emitting Diode (LED) indicator, and a manual push button that could activate the alarm, along with a Closed-Circuit Television (CCTV) camera for surveillance purposes and a wireless monitor to improve visibility. The prototype was seen to operate correctly in the experiment, where the motion sensor PIR easily sensed any movement and activated the siren alarm and the LED lights. The CCTV camera plus the wireless monitor supplemented and increased surveillance and visibility factors in the danger zone of safety. In this, this research provides a means of significant enhancement in terms of railway station safety that is through using a locally powered, solar motion sensor activation. The real-time detection and alarm of the system can potentially inform staff of potential dangers in time, thereby preventing accidents and creating a safer environment. Further research should be conducted to upgrade the capabilities and usability in different railway depot scenarios. This may involve evaluating different motion sensors, including additional safety devices, and making field tests to evaluate the system's effectiveness in real application cases.

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