# Flying Watchdog-Based Guard Patrol with Check Point Data Verification
## 4 Results and Discussion
### Light Shape Pattern Detection  
Three distinct shapes, a square, a triangle, and a pentagon, were meticulously crafted utilizing 12-volt 1-watt LED strips, each measuring 45 cm long. Detecting the light shape patterns on these objects was executed by identifying the luminous outlines encompassing each shape, employing the methodology elucidated in Section 3.2. Subsequently, we embarked on an object classification procedure to distinguish genuine identifications from erroneous ones, as exemplified in Figure 11. The segmentation process using OpenCV serves the sole purpose of identifying triangles while excluding other shapes. However, it's worth noting that the system still identifies square-shaped rooftops among the results. The next step involves applying a kernel function filtering mechanism and employing light edge detection to address this. This step eliminates false positives, as Figure 10 depicts. Upon completion of the classification process, it becomes evident that only the triangle shape meets the criteria for legitimate identification in this scenario. Verification is subsequently conducted through serial monitors to confirm the accuracy of the title.  
The subsequent phase entails the identification of three distinct pattern shapes: triangles, squares, and pentagons. This endeavor aims to determine which condition can be detected over the most extended range. To achieve this, a series of trials involving a horizontal detection approach amid various environmental objects and under diverse lighting conditions were conducted. Figure 12 illustrates the experimental setup, encompassing the image segmentation process and the application of light edge filtering. The outcomes of the detection process are showcased on the serial monitor and subsequently transmitted from NVIDIA Jetson to the Arduino microcontroller, initializing the data transmission process through laser technology.  
The detection process underwent examination under three distinct environmental lighting conditions: low light (8.9 lux), moderate light (168 lux), and standard illumination (390 lux). It is worth noting that the LED strip's luminance is 400 to 500 lux. Considering the detection distances, it becomes evident that the square shape surpasses the triangle and pentagon in terms of its range. Remarkably, the pentagon also exhibits a longer dimension than the square. Detailed findings and the outcomes of the classification process are tabulated in Table 1. These findings were validated by comparing the obtained results with the database.  
The OpenCV algorithm has proven to be a dependable tool in the detection process, and applying Gaussian Kernel filtering has yielded positive results in the object classification procedure. Choosing between four LED dots or a square shape hinges on the specific application's requirements regarding pattern usage. Both systems exhibit the remarkable capability of providing a detection range of up to 20 m, and this range can be further extended by modifying the size of the LEDs and LED strips used. The subsequent phase of this endeavor involves object tracking, where the detected object will be realigned to the  
Figure 11: Object detection capabilities: (**a**) shape detection; (**b**) filtering.  
center of the camera's field of view. This adjustment streamlines the process of transmitting data, enhancing overall efficiency.