Automated detection and classification of white blood cells (WBCs) from microscopic images play a vital role in supporting the diagnosis of hematological diseases. Accurate and robust object detection algorithms are essential for handling interclass similarities and imbalanced datasets. This study aims to evaluate and compare the performance of two modern object detection algorithms—Detection Transformer (DeTR) and YOLOv8—in performing multiclass WBC classification using public datasets from various sources with diverse visual characteristics. Five experimental scenarios were designed based on varying class distributions and data augmentation techniques, including horizontal/vertical flipping and random rotation. Both methods were trained and evaluated on the same dataset partitions, and their performances were assessed using the following standard metrics: precision, recall, and F1-score for each WBC class. The results show that YOLOv8 consistently achieved superior and more stable performance across all scenarios, with average F1-scores close to 1.00 even in augmented and imbalanced conditions. In contrast, DeTR performed competitively in balanced scenarios but showed lower consistency, particularly in classes such as Neutrophil and Monocyte. Data augmentation positively affected both models, although the gains were more prominent in YOLOv8. This study highlights the strong potential of YOLOv8 in real-time WBC classification tasks and presents DeTR as a viable yet less-optimized approach for this application. These findings contribute to the advancement of medical image-based object detection and offer valuable insights into the selection of appropriate algorithms for hematological image analysis