Development of Fingerprint-Based Motorbike Ignition System with AI-Enabled Helmet Detection / Chrystal Zhane H. Aleta, Ara Lou DM. Ancheta, Wynard I. Dela Rosa, Dave Matthew T. Diaz, Ma. Shaina R. Flocarencia.

By:

Aleta, Chrystal Zhane H

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Material type: Text

TextManila : Technological University of the Philippines, 2024Description: x, 132 pages : illustrations ; 29 cm. + 1 CD-ROM (4 3/4 in.)Content type:

text

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unmediated

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volume

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LOC classification:

BTH T 58.5 A44 2024

Dissertation note: College of Science -- Bachelor of Science in Information Technology, Technological University of the Philippines, 2024. Abstract: "With the rising number of motorbikes being a primary mode of transportation, comes the need for enhancing the security of motorbikes and rider’s safety considering that motorbike theft and accidents have been evident for the past years. This research aimed to develop an enhanced Motorbike Ignition System advancing to keyless technology and integrating AI- Enabled Helmet Detection. The system allows identification of authorized users through a Fingerprint Sensor and improves rider’s safety through Artificial Intelligence, made possible by two controllers, specifically NodeMCU and Raspberry Pi 4B. The fingerprint sensor is attached to the NodeMCU enabling the motorbike’s electrical system while the Raspberry Pi is in charge of the Helmet Detection which starts the ignition system. Factoring in that the fingerprint sensor is affected if the finger is wet/oily and if with soil/rusty. For the helmet detection, lighting is vital for its functionality. Both conditions should be satisfied, else the ignition will not start. Adding a layer of security, three (3) consecutive failed attempts of fingerprint will activate an alarm. For the helmet detection feature, EfficientDet was used as algorithm. Through Raspbian OS, Google Colab, Tensorflow, Arduino IDE, Thonny IDE and Python, the software requirements were fulfilled. Datasets were prepared in helmet detection training ensuring dependability. Model Performance Evaluation showed that the system’s Mean Average Precision (mAP) is 94.37% while the Average Precision (AP) is 0.9436502, indicating the system’s high precision. The system was also subjected to different test cases to determine needed improvements. The system was then evaluated by IT professionals, electricians, and motorists using the TUP evaluation instrument. Majority of the respondents highly accepted the system in terms of Functionality, Aesthetics, Workability, Durability, Economy and Safety. With a grand weighted mean of 3.59 interpreted as “Highly Acceptable”, the system was able to accomplish its objective, improving the current security of motorbikes and safety of the riders." -- Author's Abstract

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Holdings
Item type	Current library	Shelving location	Call number	Status	Notes	Date due	Barcode
Bachelor's Thesis COS	TUP Manila Library	Thesis Section-2nd floor	BTH T 58.5 A44 2024 (Browse shelf(Opens below))	Not for loan	For Room Use Only		BTH0004887

Thesis (Undergraduate)

College of Science -- Bachelor of Science in Information Technology, Technological University of the Philippines, 2024.

Includes bibliographical references.

"With the rising number of motorbikes being a primary mode of transportation, comes the
need for enhancing the security of motorbikes and rider’s safety considering that motorbike
theft and accidents have been evident for the past years. This research aimed to develop an
enhanced Motorbike Ignition System advancing to keyless technology and integrating AI- Enabled Helmet Detection. The system allows identification of authorized users through a
Fingerprint Sensor and improves rider’s safety through Artificial Intelligence, made possible
by two controllers, specifically NodeMCU and Raspberry Pi 4B. The fingerprint sensor is
attached to the NodeMCU enabling the motorbike’s electrical system while the Raspberry
Pi is in charge of the Helmet Detection which starts the ignition system. Factoring in that the
fingerprint sensor is affected if the finger is wet/oily and if with soil/rusty. For the helmet
detection, lighting is vital for its functionality. Both conditions should be satisfied, else the
ignition will not start. Adding a layer of security, three (3) consecutive failed attempts of
fingerprint will activate an alarm. For the helmet detection feature, EfficientDet was used as
algorithm. Through Raspbian OS, Google Colab, Tensorflow, Arduino IDE, Thonny IDE
and Python, the software requirements were fulfilled. Datasets were prepared in helmet
detection training ensuring dependability. Model Performance Evaluation showed that the
system’s Mean Average Precision (mAP) is 94.37% while the Average Precision (AP) is
0.9436502, indicating the system’s high precision. The system was also subjected to
different test cases to determine needed improvements. The system was then evaluated by
IT professionals, electricians, and motorists using the TUP evaluation instrument. Majority
of the respondents highly accepted the system in terms of Functionality, Aesthetics, Workability, Durability, Economy and Safety. With a grand weighted mean of 3.59
interpreted as “Highly Acceptable”, the system was able to accomplish its objective, improving the current security of motorbikes and safety of the riders." -- Author's Abstract

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