Convolutional Neural Network for object Identification and Detection

Andini, Silfia and Rouza, Erni and Fimawahib, Luth and Mustafa, Satria Riki and Fathoni, Ahmad and Hermanto, Teguh Iman and Nasution, Anggi Pratama (2021) Convolutional Neural Network for object Identification and Detection. In: International Syposium On Power Electronics and Control Enginering.

Text 20221- 012018 Jurnal of Physics Conference Series, 2022, 2394(1), 012018, Volume 2394.pdf Download (937kB)

Abstract

Abstract. The goal of this study is to use a Convolutional Neural Network to find the optimum architectural model for classifying cloud images. Cirrus Cumulus Stratus Nimbus uses a source dataset that includes 11 cloud classifications and 2545 cloud photos (CCSN). In this study, the best Convolutional Neural Network is retrained almost fast by transferring education from Google's basic design. Based on the modified Googlenet architecture, the training and testing phases of the classification process are divided into two. The dataset is separated into three sections during the training phase: 70% of the training data, 15% of the validation data, and 15% of the test data. There are two trials to categorize cloud photographs during the test phase, one of which has ten cloud kinds that can be randomly chosen. The precision achieved throughout the training was 44.5%, according to the findings. The results of the two tests are 75%, with an average error of 0.2. In the testing phase, the percentage is 75%. Keywords: Cloud Image, Classification, Googlenet, CNN, CCSN Dataset

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