Abstract:
This study compares the performance of current object detection models, namely YOLOv7-tiny, YOLOv8n, and EfficientDetd0,
using YOLOv5n as the baseline model in addressing the challenge of Rupiah banknote detection. The challenge involves
recognizing unique features on the banknotes, which may have higher complexity compared to common objects in object detection
tasks. The dataset used covers 2022 Emission Year Rupiah banknotes, is manually created, and covers various real-world scenarios
for comprehensive evaluation. This research also explores the impact of data augmentation to optimize model performance. Results
show that YOLOv8 is the top-performing model, with mAP@0.5 scoring 0.995 and mAP@0.5:0.95 scoring 0.994 on the test
data, also consistently maintaining high performance even without augmentation. YOLOv5 also showed impressive mAP scores
of 0.995 and 0.973 with augmentation. YOLOv7, although it did not surpass YOLOv8 and YOLOv5 in accuracy, achieved good
results, especially with data augmentation. In terms of inference time, YOLOv5 excels with 6.7 ms without augmentation and
6.5 ms with augmentation, emphasizing its efficiency. YOLOv8, although slightly less efficient, with inference time of 7.8 ms
without augmentation and 8.2 ms with augmentation, provides higher accuracy. The choice between the two depends on the balance
between accuracy and efficiency. This research also highlights the positive impact of data augmentation, especially in YOLOv5’s
responsiveness to additional data. While EfficientDet is efficient in inference time and resource usage, it suffers in performance, especially
without augmentation. This study attempts to develop a dependable method for identifying banknotes. By achieving this, the aim
was to improve accessibility in financial tasks and everyday life, particularly benefiting those with visual impairments or other disabilities.