This study addressed the challenges of limited image quality in complex production environments by propo-sing the use of transfer learning methods to aid in the convergence of models trained in production environments.The study also introduced strategies such as image super-resolution to enhance image quality and expand the target area of coarse aggregates,enabling effective segmentation and analysis of coarse aggregate images under quality constraints in pro-duction scenarios.The results demonstrate the effectiveness of the proposed strategies.The segmentation model,Model-L,not only improves the mAP value by more than 0.3 and increases the EMS value by 27％compared to Model-P trained di-rectly in production environments,but also excels in the segmentation quantity of coarse aggregates in various particle size ranges.The gradation results are reliable,with errors within 10％of mechanical sieving in the 5～25 mm range.The strat-egies proposed in this study exhibit a certain level of generality and demonstrate good segmentation performance for coarse aggregate images in specific network scenarios.
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