Thesis Bachelor of Science in Computer Engineering University of Rizal System-Morong 2024

This research study entitled Object Detection Model Traffic Light System was conducted during the School Year 2023 - 2024 in Pahati Bridge at Barangay Dalig, Teresa, Rizal. The research general objective of this research is to evaluate the developed Object Detection Model Traffic Light System. To determine the performance efficiency and level of acceptability of the study, the researchers used Intersection over Union, and mean. On the other hand, the standard deviation and weighted mean were used to determine the level of acceptability of the system. The researchers utilized a combination of developmental and descriptive methods of study. The number of respondents in this research is determined by systematic sampling. Additionally, researchers utilized a questionnaire checklist to gather data from respondents. This questionnaire checklist is designed based on ISO/IEC 25010 which includes functional suitability, performance efficiency, from the questionnaire is interpreted using a Likert scale, and ODM efficiency table. The computed mean of the object detection model efficiency is 0.8603 with an interpretation of “Very Much Efficient”. Moreover, the propagation delay resulted to mean of 0.32420 seconds. On the other hand, the results of the evaluation showed that the developed device and system was highly acceptable, with an average weighted means of 4.58, 4.55, 4.53, 4.59, 4.40, 4.56 and 4.54 from the Community which is the drivers, experts and the local government unit respectively. These findings were interpreted as "Very Much Acceptable". In this study, the researchers found that the integration of the Video Capturing Device (RPi Camera V2), Object Detection Model (Yolo V5 Nano 6), Logic Devices (Raspberry Pi 4 and Node MCU ESP8266), Wireless Communication (Comfast EW73), and Traffic Signal Light (LED) successfully performed its specified functions, getting the highest score in its usability. The researcher’s recommendation to future researchers is to make the dataset more accurate, use a higher specification Raspberry Pi, a wider range and good signal quality, and use a renewable power source. Future researchers may consider integrating additional features into the system’s hardware.