Cruz, Wency Gabriel O. <br/><br/>
Crash kita:   implementing convolutional neural network for vehicular accident detection and assistance on tirona highway, bacoor city, cavite/ 
Wency Gabriel O. Cruz, Joshua Louise B. Margallo, Maverick Rayne A. Ramos, Carl C. Senaris, and Sandryl A. Torres.-- 
 - Manila:  Technological University of the Philippines,  2025. 
 - x, 140pages:  29cm. 
<br/><br/> Bachelor's thesis<br/><br/><br/> 
<br/><br/>College of Science.--<br/> 
<br/><br/>Includes bibliographic references and index. 
<br/><br/> In the Philippines, traffic accidents are still a major issue, particularly along the<br/>Tirona Highway in Bacoor City, Cavite. The goal of this study, "CRASH KITA:<br/>Implementing Convolutional Neural Network for Vehicular Accident Detection and<br/>Assistance," is to develop a model that uses computer vision and IP cameras to<br/>automatically detect accidents. The researchers used two pre-trained YOLOv11 models to<br/>recognize different types of accidents. One focused on car-to-car collisions and reached<br/>around 84% mean average precision and 75% accuracy, while the other model included<br/>motorcycles with lower accuracy at 70%. The system includes a website made with PHP,<br/>Tailwind, and Bootstrap, where barangay authorities can view, confirm, manage<br/>accidents, and create reports. Testing showed that the system works well, but with<br/>limitations depending on the camera angle. Overall, “CRASH KITA” shows that using<br/>machine learning can help improve road safety monitoring by speeding up accident<br/>detection and response. 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Computer vision<br/>Road safety<br/> IP cameras
<br/><br/><label>LC Class. No.: </label>BTH QA 76  / C78 2025