为实现精确的粗集料级配检测，本研究提出一种便携式粗集料级配智能检测方法.采用知识蒸馏的策略对视觉大模型SAM进行网络结构轻量化，嵌入神经网络分类器PP-HGNetV2为模型提供语义判断的能力，设计粗集料颗粒特征参数数学表征算法，开发移动端应用程序，实现粗集料级配高通量检测.对5种不同粗集料级配场景进行测试，结果表明本研究方法对于粗集料颗粒的分割精度高于原始SAM模型，并且能够精确去除背景信息，粗集料颗粒关键参数提取结果准确可靠.

To achieve precise detection of coarse aggregate gradation, this study proposes an intelligent portable detection method for coarse aggregate gradation. A knowledge distillation strategy is employed to lighten the network structure of the large visual model SAM, and the neural network classifier PP-HGNetV2 is embedded to provide the model with semantic judgment capabilities. A mathematical representation algorithm for the characteristic parameters of coarse aggregate particles is designed, and a mobile application is developed to enable high-throughput detection of coarse aggregate gradation. Tests were conducted on five different coarse aggregate gradation scenarios, and the results indicate that the proposed method achieves higher segmentation accuracy for coarse aggregate particles compared to the original SAM model. It also precisely removes background information, and the extracted key parameters of coarse aggregate particles are accurate and reliable.