Comparative Evaluation of YOLOv8 Variants for Static BISINDO Alphabet Detection: Accuracy and Computational Cost Analysis
DOI:
https://doi.org/10.64366/ijids.v3i2.604Keywords:
BISINDO; YOLOv8; Object Detection; Sign Language Recognition; Model ComparisonAbstract
Indonesian Sign Language (BISINDO) plays an important role in communication for deaf communities in Indonesia. Automatic BISINDO alphabet recognition requires accurate detection while maintaining reasonable computational cost. This study evaluates the effect of model capacity on static BISINDO alphabet detection by comparing YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l under consistent experimental conditions. A dataset of 11,468 images representing 26 alphabet classes was divided into 9,168 training, 1,155 validation, and 1,145 held-out test images. All models were trained for 50 epochs using 416 × 416 pixel images on an NVIDIA RTX 2060 with 6 GB memory. Performance was evaluated using precision, recall, mAP@0.5, mAP@0.5:0.95, and training time. The validation results achieved mAP@0.5 values of 99.42%, 99.46%, 99.38%, and 99.40% for YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l, respectively, while training time increased from 0.93 to 3.59 hours. Held-out test evaluation remained consistently high, with the main errors involving the visually similar M and N gestures. Because the subsets originate from the same public dataset source, the results provide within-dataset evidence and do not establish cross-dataset or real-world generalization. Under the evaluated conditions, YOLOv8n provides the most efficient balance between detection performance and computational cost.
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