To address the challenges of dense stacking, blurred boundaries and uneven illumination of raw material pellets in the ironmaking process, this paper proposes a cascaded particle size detection and segmentation framework based on an improved RT-DETR and MobileSAM. In the detection stage, a coordinate attention （CA） mechanism is embedded into the backbone network to enhance feature extraction capabilities. Furthermore, an efficient discriminative frequency domain-based feedforward network （EDFFN） is introduced into the AIFI encoder and a multi-scale feature modulation （MFM） fusion module is employed to replace the traditional feature fusion method. Subsequently, the generated detection boxes serve as prompts to guide MobileSAM in generating initial masks. To optimize segmentation quality, a refiner incorporating dynamic receptive fields and efficient multi-scale attention (EMA) is designed for multi-scale adaptive repair of mask edges, which is then combined with geometric fitting to achieve precise particle size measurement. Experimental results on the pellet dataset demonstrate that the proposed method achieves a recall rate of 95.7% and a mIoU of 90.1%, effectively improving segmentation accuracy and mitigating over-segmentation. This study provides a high-precision and robust solution for online particle size monitoring in the industrial ironmaking process.