Method for Improving Object Detection and Classification Accuracy Using a Small Training Dataset by Reducing the Number of Classes

Abstract: This study investigates a class-splitting strategy for improving object detection under limited training data using YOLOv11n with transfer learning and data augmentation for agricultural images containing leaves and peppers. The proposed approach evaluates leaf-only, pepper-only, and combined-class configurations using mAP@0.5, mAP@0.5:0.95, precision, recall, and F1-score to examine how class splitting affects detection performance. On the small validation set used in this study, single-class training improved performance relative to the combined-class baseline, but the results should be interpreted as preliminary because the validation set contains only two samples.

Kohei Arai. “Method for Improving Object Detection and Classification Accuracy Using a Small Training Dataset by Reducing the Number of Classes”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.4 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170415

@article{Arai2026,
title = {Method for Improving Object Detection and Classification Accuracy Using a Small Training Dataset by Reducing the Number of Classes},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170415},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170415},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {4},
author = {Kohei Arai}
}