Accurate identification of protein particles in cryo-electron microscopy (Cryo-EM) images is crucial for achieving high-resolution three-dimensional (3D) structural reconstruction. However, this task faces multiple challenges, including low signal-to-noise ratios, densely distributed particles, and class imbalance. To address these issues, this study proposes a target detection algorithm named GTpick, built upon the DETR framework. GTpick introduces a cross-attention mechanism to enhance the interaction between target queries and specific image features. In addition, a grouped one-to-many label assignment strategy is employed to improve recall in densely populated regions, and a Focal Loss function is incorporated to mitigate the adverse effects of background noise and class imbalance on detection accuracy. Experiments on large-scale Cryo-EM datasets demonstrate that GTpick outperforms existing machine learning-based particle-picking methods in terms of the resolution of 3D density maps reconstructed from detected particles and achieves superior Recall and F1 scores, particularly excelling in the Recall metric.