  In a recent study, we found that publicly BCCD and BCD datasets have
significant issues such as labeling errors, insufficient sample size, and poor
data quality. To address these problems, we performed sample deletion,
re-labeling, and integration of these two datasets. Additionally, we introduced
the PBC and Raabin-WBC datasets, and ultimately created a high-quality,
sample-balanced new dataset, which we named TXL-PBC. The dataset contains 1008
training sets, 288 validation sets, and 144 test sets. Firstly, The dataset
underwent strict manual annotation, automatic annotation with YOLOv8n model,
and manual audit steps to ensure the accuracy and consistency of annotations.
Secondly, we addresses the blood cell mislabeling problem of the original
datasets. The distribution of label boundary box areas and the number of labels
are better than the BCCD and BCD datasets. Moreover, we used the YOLOv8n model
to train these three datasets, the performance of the TXL-PBC dataset surpass
the original two datasets. Finally, we employed YOLOv5n, YOLOv5s, YOLOv5l,
YOLOv8s, YOLOv8m detection models as the baseline models for TXL-PBC. This
study not only enhances the quality of the blood cell dataset but also supports
researchers in improving models for blood cell target detection. We published
our freely accessible TXL-PBC dataset at
https://github.com/lugan113/TXL-PBC\_Dataset.
