Pengembangan YOLO untuk Mengunci Sebuah Obyek Telapak Tangan Diantara Sejumlah Objek Seragam
Abstract
The hand is one part of the body that is often used in daily human activities. Nowadays, in line with the development of digital technology and computing technology, the role of hands has the potential to become even wider. Hands can potentially be used to model operational facilities or control input models for a device. Problems arise if more than one visual of a hand is captured by the camera. This has the potential to cause ambiguity due to the emergence of multiple hand gesture control input models. It is necessary to lock or tag the original hand control input model in such a way that the other hand can be ignored. In this research, the Google Media Pipe Hand (GMPH) framework is used to mark the hand area based on the input image and the YOLO framework will work to recognize the hand with a marker, which will lock the marked hand between the visuals of other hands. Based on test results involving 800 test data in the form of video data with visual images of one hand gesture or more than one hand gesture image, it is known that the results of the YOLO modification show success with an accuracy of 97.5%.
Keywords: Hand; Markers; Google Media Pipe Hand; YOLO Framework
Abstrak
Tangan merupakan salah satu anggota tubuh yang cukup sering dipergunakan dalam keseharian aktifitas manusia. Dewasa ini sesuai dengan perkembangan teknologi digital dan teknologi komputasi maka peran tangan berpotensi menjadi lebih luas lagi. Tangan berpotensi dipergunakan untuk model sarana operasional atau model input kendali terhadap suatu perangkat. Permasalahan muncul bila suatu visual tangan yang tertangkap oleh kamera berjumlah lebih dari satu. Hal ini berpotensi menimbulkan ambigu karena munculnya model input kendali gestur tangan ganda. Perlu dilakukan proses penguncian atau penandaan pada satu model input kendali tangan yang asli sedemikian rupa sehingga tangan yang lain dapat diabaikan. Pada peelitian ini, framework Google Media Pipe Hand (GMPH) dipergunakan untuk menandai area tangan berdasarkan citra input dan framework YOLO akan bekerja untuk mengenali tangan dengan marker, yang akan megunci tangan bermarker diantara visual tangan yang lain. Berdasarkan hasil pengujian yang melibatkan 800 data uji berupa data video dengan visual citra satu gestur tangan maupun lebih dari satu citra gestur tangan, diketahui bahwa hasil modifikasi YOLO menunjukkan keberhasilan dengan akurasi 97,5%.
Kata kunci: Tangan; Marker; Google Media Pipe Hand; Framework YOLO
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