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Research Article | Open Access |

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

Author 1: Kohei Arai
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 17, No. 4 · Published 2026

DOI: https://doi.org/10.14569/IJACSA.2026.0170415

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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.

Keywords

YOLOv11n COCO2017 HSV/Flip/Crop transfer learning pipeline SPDarknet C2PSA Self-Attention SPPF

How to Cite this Article

Arai, K. (2026). 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, 17(4). https://doi.org/10.14569/IJACSA.2026.0170415

Arai, Kohei. "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, vol. 17, no. 4, 2026, https://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},
  volume    = {17},
  number    = {4},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai},
  doi       = {10.14569/IJACSA.2026.0170415},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170415}
}

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