Abstract
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.
Highlights
Ranah pengenalan dan pendeteksian objek [1]–[3], pada Computer Vision saat ini sedang berkembang pesat dan mulai diterapkan di berbagai bidang, dari industri hingga medis
Object recognition and detection have been in demand by many parties since Computer Vision in the 1960s, both in the industrial and medical fields
Many studies have focused on object recognition and detection with various types of algorithm models that can recognize and detect objects in an image
Summary
Ranah pengenalan dan pendeteksian objek [1]–[3], pada Computer Vision saat ini sedang berkembang pesat dan mulai diterapkan di berbagai bidang, dari industri hingga medis. Mulai dari Region based Convolutional Neural networks (R-CNN) [15], [16] yang ditemukan oleh Ross Girshick, Spatial Pyramid Pooling Network (SPP-Net) oleh K. He et al, fast RCNN, Faster R-CNN [17], [18], [19], You only look once (YOLO) [20]–[23] hingga RetinaNet [24], [25]. Susunan penelitian ini yaitu bagian kedua akan memperkenalkan beberapa hal penting untuk dapat memahami model yang akan dibahas, bagian ketiga akan meneliti mengenai beberapa model, bagian keempat akan membandingkan mengenai model-model yang telah diteliti sebelumnya, dan bagian kelima akan menyimpulkan topik bahasan
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