Application of Deep Learning Convolution Neural Network Method on KRSBI Humanoid R-SCUAD Robot
DOI:
https://doi.org/10.12928/biste.v2i1.985Keywords:
Deep Learning, CNN, Robot Humanoid, Tensor FlowAbstract
In a soccer game the ability of humanoid robots that one needs to have is to see the ball object in real time. Development of the ability of humanoid robots to see the ball has been developed but the level of accuracy of object recognition and adaptation during matches still needs to be improved. The architecture designed in this study is Convolutional Neural Network or CNN which is designed to have 6 hidden layers with implementation of the robot program using the Tensorflow library. The pictures taken are used in the training process to have 9 types of images based on where the pictures were taken. Each type of image is divided into 2 classes, namely 2000 images for ball object classes and 2000 images for non-ball object classes. The test is done in real time using a white ball on green grass. From the architectural design and white ball detection test results obtained a success rate of 67%, five of the nine models managed to recognize the ball. The model can recognize objects with an image processing speed of a maximum of 13 FPS.
Dalam pertandingan sepak bola kemampuan robot humanoid yang perlu dimiliki salah satunya adalah melihat objek bola secara real time. Pengembangan kemampuan robot humanoid untuk melihat bola telah dikembangkan tetapi tingkat akurasi pengenalan objek dan adaptasi saat pertandingan masih perlu ditingkatkan. Arsitektur yang dirancang pada penelitian ini yaitu Convolutional Neural Network atau CNN yang dirancang memiliki 6 hidden layer dengan implementasi pada program robot menggunakan library Tensorflow. Gambar yang diambil digunakan dalam proses training memiliki 9 jenis gambar berdasarkan tempat pengambilan gambar. Tiap jenis gambar terbagi menjadi 2 class yaitu 2000 gambar untuk class objek bola dan 2000 gambar untuk class objek bukan bola. Pengujian dilakukan secara real time dengan menggunakan bola berwarna putih di atas rumput hijau. Dari perancangan arsitektur dan hasil pengujian pendeteksian bola putih didapatkan persentase keberhasilan 67% yaitu lima dari sembilan model berhasil mengenali bola. Model dapat mengenali objek dengan kecepatan pengolahan gambar adalah maksimal 13 FPS.
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