This paper addresses key limitations in current Semi-Supervised Object Detection (SSOD) frameworks, focusing on issues related to pseudo-label quality, confidence bias, and inefficient query generation. Traditional methods, including CNN-based and DETR-based architectures, often face challenges such as noisy pseudo-labels, overfitting to common object categories, and consequently face difficulty detecting rare objects. Specifically, recent DETR-based SSOD approaches struggle with the one-to-many assignment strategy, which produces noisy pseudo-labels and overlapping predictions, resulting in suboptimal performance. To address these challenges, we propose STEP-DETR, a transformer-based SSOD framework. STEP-DETR introduces Super Teacher to generate higher-quality pseudo-labels and improve the student’s learning process. Furthermore, STEP-DETR proposes Pseudo-Label Text Queries, which incorporate text embeddings from Super Teacher, balancing the student’s confidence across common and rare categories, thereby mitigating confidence bias and enhancing generalization. Moreover, Denoising Text Guided Object Queries synthesizes query-label pairs for foreground and background using contrastive learning, enabling the model to better distinguish objects from background noise. To further boost performance and training efficiency, a Query Refinement Module is incorporated to filter out redundant denoising queries. On MS-COCO and Pascal VOC benchmarks, STEP-DETR outperforms state-of-the-art methods, demonstrating its effectiveness in improving semi-supervised object detection. Notably, with just 10% labeled data, it achieves 45.4 mAP, surpassing the baseline Semi-DETR by 1.9 mAP.