# Flying Watchdog-Based Guard Patrol with Check Point Data Verification
## 2 Related Work  
In the present study, the prevailing paradigm in drone-based security patrol systems revolves around path planning for individual drones and swarms, deploying detection methodologies for intruder discrimination, and utilizing sensor technologies optimized for low-light environments. Conventional checkpoint data validation, particularly concerning patrol check-in processes, predominantly relies on RFID technology. This research introduces an innovative approach employing a laser as a singular, concentrated beam for high-speed data transmission. A computer vision-based sensing apparatus is also implemented to recognize traffic signal colors.  
The patrolling method can identify suspicious behavior by utilizing single or multiple object optimization techniques to anticipate human movement [24; 25]. This approach leverages heuristic search strategies to explore the impact of various intruder behaviors on search performance [26; 27]. Critical parameters in the pattern of search within this method encompass the size of the search area, the number of patrolling drones, camera positioning, and the behavior of potential intruders. In the context of night-time security and intruder detection, an infrared camera is employed with drones [28]. Furthermore, the study delves into the analysis of human behavior, specifically focusing on gait and even hand movements, by compiling a dataset encompassing diverse walking styles [29]. The primary research objectives revolve around night-time object detection, human behavior analysis, and intruder warning systems. Another facet of this field of study involves path planning utilizing multiple Unmanned Aerial Vehicles (UAVs) for intricate patrol missions [30]. The findings demonstrate that a strategy involving multi-layer nesting and random walk patterns outperforms the particle swarm optimization algorithm in the context of drone patrolling. The utilization of drones in security operations remains primarily centered on their capacity for search and detection, as evidenced by prior research. While incapable of entirely autonomous flight, drones are supportive tools that enhance security measures. This study, however, shifts its focus towards a novel perspective, emphasizing the role of patrol drones in actively safeguarding and fortifying security efforts.  
Regarding light detection, the prevailing emphasis often centers on color detection monitoring, which finds applications in recognizing traffic lights for autonomous driving purposes and monitoring RGB images to assess light pollution levels [31; 32; 33]. The results of these investigations reveal that both OpenCV and the You Only Look Once (YOLO) algorithm are instrumental in detecting traffic lights and recognizing their colors through trained models [34]. Deep learning techniques, specifically those based on integral channel features, can identify shape, color, and texture attributes associated with traffic lights. Additionally, YOLOv4 and YOLOv5 demonstrate exceptional success rates exceeding 85% in detecting and recognizing traffic lights [35; 36]. Besides computer vision, another approach to night-time illumination detection involves assessing night ground brightness with optical devices for light mapping [37]. The existing research shows different techniques for detection with different algorithms depending on the application. The findings from prior studies underscore the significance of refining the filtering process to mitigate light dispersion effects. Many of these investigations are still ongoing, with a primary focus on enhancing process accuracy and minimizing the occurrence of false positives in target identification.