Tang Yu, Zhao Shixiang, Qin Hui, Ming Pan, Fang Tianxing, Zeng Jinyuan
School of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China.
Nanjing Hydraulic Research Institute, Nanjing 210029, China.
Sensors (Basel). 2025 Aug 4;25(15):4797. doi: 10.3390/s25154797.
Rockfill particle gradation significantly influences mechanical performance in earth-rockfill dam construction, yet on-site screening is often time-consuming, labor-intensive, and structurally invasive. This study proposes a rapid and non-destructive detection method using mobile-based photography and an end-to-end image segmentation approach. An enhanced YOLOv8-seg model with an integrated dual-attention mechanism was pre-trained on laboratory images to accurately segment densely stacked particles. Transfer learning was then employed to retrain the model using a limited number of on-site images, achieving high segmentation accuracy. The proposed model attains a mAP50 of 97.8% (base dataset) and 96.1% (on-site dataset), enabling precise segmentation of adhered and overlapped particles with various sizes. A Minimum Area Rectangle algorithm was introduced to compute the gradation, closely matching the results from manual screening. This method significantly contributes to the automation of construction workflows, cutting labor costs, minimizing structural disruption, and ensuring reliable measurement quality in earth-rockfill dam projects.
堆石料颗粒级配在土石坝施工中对力学性能有显著影响，但现场筛分往往耗时、费力且对结构有侵入性。本研究提出一种基于移动摄影和端到端图像分割方法的快速无损检测方法。一种具有集成双注意力机制的增强型YOLOv8-seg模型在实验室图像上进行预训练，以准确分割密集堆积的颗粒。然后采用迁移学习，使用有限数量的现场图像对模型进行重新训练，实现了较高的分割精度。所提出的模型在基础数据集上的mAP50为97.8%，在现场数据集上为96.1%，能够精确分割各种尺寸的粘附和重叠颗粒。引入最小面积矩形算法来计算级配，与人工筛分结果紧密匹配。该方法对施工流程的自动化有显著贡献，降低了劳动力成本，减少了结构破坏，并确保了土石坝项目中可靠的测量质量。
