Application of Deep Learning Convolution Neural Network Method on KRSBI Humanoid R-SCUAD Robot

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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Efficient convolutional neural network with multi-kernel enhancement features for real-time facial expression recognition

Journal of Real-Time Image Processing ◽

10.1007/s11554-021-01088-w ◽

2021 ◽

Author(s):

Minze Li ◽

Xiaoxia Li ◽

Wei Sun ◽

Xueyuan Wang ◽

Shunli Wang

Keyword(s):

Neural Network ◽

Facial Expression ◽

Convolutional Neural Network ◽

Real Time ◽

Facial Expression Recognition ◽

Expression Recognition

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A Lightweight Deep Convolutional Neural Network Model for Real-Time Age and Gender Prediction

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC) ◽

10.1109/icaecc50550.2020.9339503 ◽

2020 ◽

Author(s):

Md. Nahidul Islam Opu ◽

Tanha Kabir Koly ◽

Annesha Das ◽

Ashim Dey

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real Time ◽

Network Model ◽

Neural Network Model ◽

Deep Convolutional Neural Network ◽

Age And Gender ◽

And Gender

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Face Detection in Real Time Live Video Using Yolo Algorithm Based on Vgg16 Convolutional Neural Network

2021 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) ◽

10.1109/icieam51226.2021.9446291 ◽

2021 ◽

Author(s):

Htet Aung ◽

Alexander V. Bobkov ◽

Nyan Lin Tun

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real Time ◽

Face Detection ◽

Live Video

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Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection

2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA) ◽

10.1109/icmla51294.2020.00030 ◽

2020 ◽

Author(s):

William Thomson ◽

Neelanjan Bhowmik ◽

Toby P. Breckon

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real Time ◽

Fire Detection ◽

Network Architectures ◽

Neural Network Architectures

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Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron

Sensors ◽

10.3390/s21144916 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4916

Author(s):

Ali Usman Gondal ◽

Muhammad Imran Sadiq ◽

Tariq Ali ◽

Muhammad Irfan ◽

Ahmad Shaf ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real Time ◽

Multilayer Perceptron ◽

Smart Cities ◽

Classification Model ◽

Rapid Urbanization ◽

Metal Waste ◽

Waste Classification ◽

The City

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.

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