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.--

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Cruz, Wency Gabriel O [author]

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TextPublication details: Manila: Technological University of the Philippines, 2025.Description: x, 140pages: 29cmContent type:

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BTH QA 76 C78 2025

Dissertation note: College of Science.-- Bachelor of science in computer science: Technological University of the Philippines, 2025. Summary: In the Philippines, traffic accidents are still a major issue, particularly along the Tirona Highway in Bacoor City, Cavite. The goal of this study, "CRASH KITA: Implementing Convolutional Neural Network for Vehicular Accident Detection and Assistance," is to develop a model that uses computer vision and IP cameras to automatically detect accidents. The researchers used two pre-trained YOLOv11 models to recognize different types of accidents. One focused on car-to-car collisions and reached around 84% mean average precision and 75% accuracy, while the other model included motorcycles with lower accuracy at 70%. The system includes a website made with PHP, Tailwind, and Bootstrap, where barangay authorities can view, confirm, manage accidents, and create reports. Testing showed that the system works well, but with limitations depending on the camera angle. Overall, “CRASH KITA” shows that using machine learning can help improve road safety monitoring by speeding up accident detection and response.

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Holdings
Item type	Current library	Shelving location	Call number	Copy number	Status	Date due	Barcode
Bachelor's Thesis CIT	TUP Manila Library	Thesis Section-2nd floor	BH QA 76 C78 2025 (Browse shelf(Opens below))	c.1	Not for loan		BTH0006623

Bachelor's thesis

College of Science.--
Bachelor of science in computer science: Technological University of the Philippines,
2025.

Includes bibliographic references and index.

In the Philippines, traffic accidents are still a major issue, particularly along the
Tirona Highway in Bacoor City, Cavite. The goal of this study, "CRASH KITA:
Implementing Convolutional Neural Network for Vehicular Accident Detection and
Assistance," is to develop a model that uses computer vision and IP cameras to
automatically detect accidents. The researchers used two pre-trained YOLOv11 models to
recognize different types of accidents. One focused on car-to-car collisions and reached
around 84% mean average precision and 75% accuracy, while the other model included
motorcycles with lower accuracy at 70%. The system includes a website made with PHP,
Tailwind, and Bootstrap, where barangay authorities can view, confirm, manage
accidents, and create reports. Testing showed that the system works well, but with
limitations depending on the camera angle. Overall, “CRASH KITA” shows that using
machine learning can help improve road safety monitoring by speeding up accident
detection and response.

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