Design of Image Processing-Based Painting Museum Security Monitoring Design Using the YOLOv5 Method
DOI:
https://doi.org/10.33795/jartel.v14i3.5272Keywords:
Image Processing, YOLOv5, Museum Security, Object Detection, Raspberry PiAbstract
Paintings are valuable art assets in museums and are highly vulnerable to physical damage caused by direct human contact. Conventional surveillance systems such as CCTV rely heavily on manual monitoring and often fail to provide rapid responses to violations. This study proposes an image processing based museum security monitoring system using the YOLOv5 method to automatically detect hand movements approaching or entering the painting area. The system is implemented as a prototype using a Raspberry Pi 4, a webcam as visual input, and integrated outputs in the form of a web based monitoring interface, Telegram notifications, and audio warnings. System performance is evaluated by testing various camera distances and viewing angles to determine optimal placement. Experimental results show that the best detection performance is achieved at a distance of 1 meter with a camera angle of 45 degrees, producing an average detection accuracy of 67.2 to 68.8 percent. The average object detection processing time is 0.11 milliseconds, indicating that the system is capable of real time operation despite hardware limitations. The proposed system demonstrates the feasibility of automated museum security monitoring using lightweight object detection models on embedded platforms.
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