Mohamed Osman, Baloola Mohamed (2022) Development of drone-based solution for medicine delivery using face recognition and guidance landing system / Mohamed Osman Baloola Mohamed. Masters thesis, Universiti Malaya.
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Abstract
This thesis presents the Development OF Drone-Based Solution for Medicine Delivery Using Face Recognition and Guidance Landing System. The drone is equipped with a Sanitiser Unit (SU) and a secure Delivery Box (DB) for contactless medication delivery using face recognition and the GLS. The development of the drone GLS system consists of two microcontroller sensor circuits that visualise the delivery area's lighting conditions to improve efficiency. In addition to the SU and DB, the drone GLS system also has a Motion Detection Unit (MDU) and a Voice Command Guiding Unit (VCGU) for medication delivery. The first development board is the Arduino Uno, which controls the face recognition camera, DB, SU, MDU, and VCGU units. The second development board is the ESP32 DevKitC V4, which controls the GLS. An Internet of Things (IoT) microcontroller is connected to the Internet using IoT mobile apps. GLS is combined with four light-dependent resistors and a light-intensity sensor. Those sensors visualise the light conditions to enhance face recognition, and a GY-30 light intensity sensor is used to measure the value of the illumination. The drone Facial Recognition Camera (FRC) and GLS system has been tested on 5001 animal faces, 5030 static human photos inclusive of 5000 photos from Flickr-Faces-HQ, 30 actual photos, and 35 real human volunteers' faces. The overall accuracy of the face recognition system is 98.53%. The GLS has enhanced the detection distance to almost double and increased the detection distance to 1.47 metres. A strong correlation was found between face recognition distance, light direction, illumination, and light colour temperature (p<0.05). The drone GLS system can detect real human face recognition with high accuracy, and the development is for the purpose of performing medication delivery outdoors.
| Item Type: | Thesis (Masters) |
|---|---|
| Additional Information: | Dissertation (M.A) - Faculty of Engineering, Universiti Malaya, 2022. |
| Uncontrolled Keywords: | Drone; Face recognition; Guidance landing system; Light direction; Light intensity |
| Subjects: | R Medicine > R Medicine (General) T Technology > T Technology (General) |
| Divisions: | Faculty of Engineering |
| Depositing User: | Mrs Rafidah Abu Othman |
| Date Deposited: | 27 Mar 2024 02:06 |
| Last Modified: | 27 Mar 2024 02:07 |
| URI: | http://studentsrepo.um.edu.my/id/eprint/14862 |
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