Alat Bantu Pendeteksi Objek Untuk Tuna Netra Berbasis Ai Mobilenet Pada Raspberry Pi 3B

Sofia Ariyani, Aji Brahma Nugroho, Ahmad Syarif Toyyib Mubarok

Abstract


Abstract - Over time, technology for disability aids is also growing rapidly around the world. One of the technologies that has a function like the eye is the camera. While in other technologies, now has been created a small PC / computer that can be used as a microcontroller, called a Mini PC modeled Raspberry Pi, but if only using these two tools simple object detection system becomes less complete because the system can only detect objects without knowing the distance, therefore, in order for the distance from an object to be read by the system, the HC SR04 sensor is used which is compatible with raspberry pi, to make this system also needed a data image processing system so that the system can detect objects, in this final task using a pre-trained model of mobilenet, mobilnet is one of the convolutional neural network (CNN) architectures that can be used to overcome the need for excessive computing resources. In this final task the system can detect as many as 80 objects, but in the system test used 5 objects with 50 types of variants, namely bottle objects, glass objects, book objects, people objects, and mobile phone objects, testing was conducted as much as 3 times from each variant of the object with 3 different distances as a determining factor of accuracy, namely distance 100cm, distance 150cm, and distance 200cm, From the results of object detection tests that have been done obtained the average percentage of object detection by 67%, the most accurate objects that can be read by the system at all distances are people's objects, while for other objects only read accurately at a distance of 100cm only.


Keywords


Raspberry Pi; Mobilenet; HC SR04 Sensor; Camera

References


F. Sindy, “Pendeteksian Objek Manusia Secara Realtime Dengan Metode MobileNet-SSD Menggunakan Movidius Neural Stick pada Raspberry Pi,” p. 77, 2019.

I. S. Walingkas et al., “Perpaduan Sensor Ultrasonik Dengan Mini Computer Raspberry Pi Sebagai Pemandu Robot Beroda,” vol. 8, no. 3, pp. 121–132, 2019.

A. N. Syahrudin and T. Kurniawan, “Input Dan Output Pada Bahasa,” J. Dasar Pemrograman Python STMIK, no. January, pp. 1–7, 2018.

F. Martunus, “Implementasi Face Recognition Dengan Opencv Pada ‘Smart Cctv’ Untuk Keamanan Brankas Berbasis Iot,” 2020.

F. Evan, “PENERAPAN IMAGE CLASSIFICATION DENGAN PRE-TRAINED MODEL MOBILENET DALAM CLIENT-SIDE MACHINE LEARNING PEN ` ERAPAN IMAGE CLASSIFICATION DENGAN PRE-TRAINED MODEL MOBILENET DALAM CLIENT-SIDE MACHINE LEARNING,” 2020.




DOI: https://doi.org/10.32528/elkom.v4i1.4951

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Jurnal Teknik Elektro dan Komputasi (ELKOM)

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

View My Status                                                                       Indexing Service

                              

UNMUH

   Publisher :
   UNIVERSITAS MUHAMMADIYAH JEMBER
   Jl. Karimata No. 49 Jember 68121 East Java
   Website : www.unmuhjember.ac.id
   Email : kantorpusat@unmuhjember.ac.id

Editorial Address :
Electrical Engineering
Faculty of Engineering
UNIVERSITAS MUHAMMADIYAH JEMBER
Jl. Karimata No. 49 Jember 68121 East Java

slot gacor slot gacor hari ini slot gacor 2025 demo slot pg slot gacor slot gacor