Deteksi dan Pengenalan Objek Dengan Model Machine Learning: Model Yolo

Penulis

  • Qurotul Aini University of Raharja
  • Ninda Lutfiani University of Raharja
  • Hendra Kusumah University of Raharja
  • Muhammad Suzaki Zahran University of Raharja

DOI:

https://doi.org/10.24114/cess.v6i2.25840

Kata kunci:

object detection, YOLO, object recognition, algorithm, efficiency

Abstrak

Object recognition and detection have been in request by numerous parties since Computer Vision innovation within the 1960s, both within the industrial and medical area. Since then, many studies have focused on object recognition and detection with various types of algorithm models that can recognize and detect objects in an image. However, not all of these algorithm models are efficient and effective in their application. Most of the previous algorithm models have a relatively high level of complexity. Here, the author tries to explain and introduce the YOLO (You only look once) algorithm model, which has a high enough image detection processing speed capability and accuracy that can compete with the previous algorithm models. There are several advantages and disadvantages of each version made, which are explained in the discussion section.

Unduhan

Data unduhan tidak tersedia.

Biografi Penulis

  • Qurotul Aini, University of Raharja
    Program Magister Departemen Informatika, Fakultas Sains dan Teknologi, Universitas Raharja
  • Ninda Lutfiani, University of Raharja
    Program Magister Departemen Informatika, Fakultas Sains dan Teknologi, Universitas Raharja
  • Hendra Kusumah, University of Raharja
    Program Magister Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Raharja
  • Muhammad Suzaki Zahran, University of Raharja
    Program Studi Sistem Komputer, Fakultas Sains dan Teknologi, Universitas Raharja

Referensi

Diterbitkan

2021-07-31

Terbitan

Bagian

Articles

Cara Mengutip

Deteksi dan Pengenalan Objek Dengan Model Machine Learning: Model Yolo. (2021). CESS (Journal of Computer Engineering, System and Science), 6(2), 192-199. https://doi.org/10.24114/cess.v6i2.25840

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