Balinese Shadow Puppet Characters Detection In The Wayang Peteng Performance Using The Yolov5 Algorithm
DOI:
https://doi.org/10.23887/janapati.v12i3.65906Keywords:
computer vision, object detection, wayang, yoloAbstract
To generate greater public interest in Balinese shadow puppet performances, it is crucial to explore novel ways of educating viewers about the characters showcased in the plays, as many individuals may need to become more familiar with them. In Object Detection, an algorithm is called You Only Look Once (YOLO). This research utilizes the YOLOv5 algorithm to detect Balinese shadow puppet characters in the "wayang peteng" performances. The dataset consists of 5040 images, divided into training, validation, and test data, with a ratio of 7:2:1 (This ratio helps in effectively training and evaluating the YOLOv5 model on a diverse set of data). Four YOLO models are trained, each with a different number of epochs (a single iteration of training when the entire dataset has been passed forward and backward through the neural network), resulting in 12 models. All models are tested using the test data images to obtain precision, recall, and mean Average Precision (mAP) metrics. Additionally, three videos measure the average frames processed per second. The research findings reveal that the YOLOv5n model with 200 epochs achieves the best results, with a precision value of 1, recall of 1, mAP@0.5 of 0.995, mAP@0.5-0.95 of 0.985, and 128.20 frames per second.
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