# Flying Watchdog-Based Guard Patrol with Check Point Data Verification
## 1 Introduction
Additionally, a D350 depth-sensing camera integrated with the drone facilitates the identification of three distinct patterns generated by LEDs of different colors. The experimental results will demonstrate the system's detection range, the lumens of LEDs detectable under diverse low-light environmental conditions, detection algorithms, and the optimization of detector response time. The drone has a laser transmitter featuring an 8-bit unique code exclusively for PGP authentication. The PGP, the check-in station, incorporates a self-powering mechanism through solar panels. It functions to activate the LED pattern and serve as the laser receiver while offering real-time remote monitoring capabilities to manage LED activation times, check-in times, and log-book data. Real-time monitoring also enables the possibility of triggering alarms in the event of patrol negligence. The number of PGPs can be tailored to meet the specific requirements of the surveillance operation, accommodating a variable quantity of checkpoints and perimeter locations for drone inspection. Verification entails conducting drone flights following predefined flight patterns with multiple checkpoints to ensure the system's reliability. The flight and check-in process can be conveniently monitored through remote access, ensuring the system's robustness and effectiveness in enhancing surveillance operations. This system introduces an innovative approach featuring a specially designed luminous marker capable of being detected by a vision sensor, thereby supplanting the need for traditional QR codes or tags. Tags typically serve as reference points in drone applications, facilitating specific tasks like drone landings. However, these conventional tags face limitations in low-light conditions. The primary challenge is the development of a detection algorithm capable of operating optimally in favorable conditions, utilizing a compact LED emitting a small dot of light and following a particular LED pattern. Our study aims to assess the system's performance across varying environmental lighting conditions to gauge its reliability. We will also investigate how LED color affects detection distance when combined with the detection algorithm and the efficacy of the filtering process in distinguishing genuine targets from false ones. This innovative approach draws inspiration from a detection model initially designed for identifying traffic lights in autonomous driving scenarios. Additionally, we explore the groundbreaking concept of information transmission via laser technology and its integration into drone systems. This research encompasses various aspects, including the alignment process between the laser transmitter and receiver, data transmission time, and the impact of ambient light on laser-based data transmission. This multifaceted investigation promises to yield valuable insights into the practicality and effectiveness of these novel techniques. The remaining sections of this study are structured as follows. In Section 2, we explore prior research, providing valuable context to better appreciate our contributions' uniqueness. Moving on to Section 3, we present an in-depth overview of the system's design and architecture, elucidating the goals we have set for our proposed method. Section 4 comprehensively presents the test results and a thorough drone and PGP system analysis. Finally, in Section 5, we draw our conclusions and discuss avenues for future work.