2022
DOI: 10.12928/telkomnika.v20i5.23464
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Official logo recognition based on multilayer convolutional neural network model

Abstract: Deep learning has gained high popularity in the field of image processing and computer vision applications due to its unique feature extraction property. For this characteristic, deep learning networks used to solve different issues in computer vision applications. In this paper the issue has been raised is classification of logo of formal directors in Iraqi government. The paper proposes a multi-layer convolutional neural network (CNN) to classify and recognize these official logos by train the CNN model on s… Show more

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Cited by 5 publications
(5 citation statements)
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“…With the popularization of digital imaging systems, image recognition has witnessed substantial advancements through machine learning in AI. In addition, with the advent of advanced deep learning techniques, time-consuming manual labeling processes, and slow machine learning processes have been revolutionized into a new computational process [14], [17]. Deep learning can autonomously derive features and learn independently from only the raw data provided by the operators.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…With the popularization of digital imaging systems, image recognition has witnessed substantial advancements through machine learning in AI. In addition, with the advent of advanced deep learning techniques, time-consuming manual labeling processes, and slow machine learning processes have been revolutionized into a new computational process [14], [17]. Deep learning can autonomously derive features and learn independently from only the raw data provided by the operators.…”
Section: Methodsmentioning
confidence: 99%
“…Initially, emotion recognition training relied on manual labeling. However, with the integration of systems and information technology, machine learning techniques have gained prominence, leading to the development of deep learning techniques [13], [14]. The advantage of deep learning lies in its ability to achieve training and verification with a small sample size within a short time [15].…”
Section: Introductionmentioning
confidence: 99%
“…Penelitian lain yang dilakukan oleh [2], yaitu melakukan pendeteksian jenis mobil menggunakan metode YOLO dan Faster R-CNN, YOLO mendapatkan akurasi yang lebih rendah dibandingkan Faster R-CNN karena YOLO lebih banyak tidak mendapatkan region saat melakukan prediksi dibandingkan Faster-RCNN. Penelitian lain dilakukan oleh [3], yang dalam penelitiannya membuat sistem pengenalan logo resmi pemerintah Iraq menggunakan metode Multilayer Convolutional Neural Network, pada penelitian ini didapatkan akurasi sebesar 99,16%. Penelitian lain dilakukan oleh [4], pembuatan modul deteksi objek manusia menggunakan metode YOLO dan mendapatkan hasil bahwa YOLOv4 mampu untuk mendeteksi objek manusia dengan tingkat akurasi sebesar 87,03%.…”
Section: Metode Penelitianunclassified
“…The improvement of technology and popularization of big data analysis have facilitated the prevalence of video surveillance systems constructed with artificial intelligence (AI) deep learning [1]- [6], [7]- [10]. Because deep-learning methods require only a small number of samples and can complete training and validation within a short period of time, they are used in the recognition of human activities, facial expressions, and voices.…”
Section: Introductionmentioning
confidence: 99%