THESIS

Performance Analysis of YOLOv11 in Rice Sack Detection and Classification

ABSTRACT

This research aims to develop a deep learning-based object detection system using the YOLOv11 architecture to identify types of rice sacks: “Udang Premium,” “Udang Kuning,”‘ and “Udang Hitam”. The innovation of this study lies in exploring the impact of two different annotation methods, namely polygon and bounding box, on detection accuracy. The dataset comprised 169 images with 325 annotations, initially exhibiting significant class imbalance, particularly for the “Udang Kuning” class. The model was trained and evaluated using mAP (mAP), Precision, and Recall metrics. Experimenta results demonstrate excellent performance, with mAP@50 reaching 94% on the data validasi and 97% on the data pengujian. Per-class detection showed an Average Precision (AP) of 100% for “Udang Kuning” and “Udang Premium” on the data pengujian, and 91% for “Udang Hitam”. Confusion Matrix analysis confirmed a higl number of True Positives and minimal False Negatives, although some False Positives and one misclassification case were observed for the “Udang Hitam” class. A comparison with other object detection methods, namely RF-DETR (Base) and YOLOv12 (Fast), revealed that YOLOv11 consistently outperformed both in terms of mAP @50 (93.8% vs. 92.2% and 91.6%), Precision (95.8% vs. 95.1% and 93.5%). and Recall (92.6% vs. 81.0% and 89.7%). These findings affirm that YOLOv11 is a highly effective solution for rice sack detection, with the potential to enhance automation, and efficiency in identification and sorting processes within related industries.

PUBLICATION

Under Review