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A graph attention reasoning model for prefabricated component detection
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2025-01-02 , DOI: 10.1111/mice.13373
Manxu Zhou, Guanting Ye, Ka‐Veng Yuen, Wenhao Yu, Qiang Jin
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2025-01-02 , DOI: 10.1111/mice.13373
Manxu Zhou, Guanting Ye, Ka‐Veng Yuen, Wenhao Yu, Qiang Jin
Accurately checking the position and presence of internal components before casting prefabricated elements is critical to ensuring product quality. However, traditional manual visual inspection is often inefficient and inaccurate. While deep learning has been widely applied to quality inspection of prefabricated components, most studies focus on surface defects and cracks, with less emphasis on the internal structural complexities of these components. Prefabricated composite panels, due to their complex structure—including small embedded parts and large‐scale reinforcing rib—require high‐precision, multiscale feature recognition. This study developed an instance segmentation model: a graph attention reasoning model (GARM) for prefabricated component detection, for the quality inspection of prefabricated concrete composite panels. First, a dataset of prefabricated concrete composite components was constructed to address the shortage of existing data and provide sufficient samples for training the segmentation network. Subsequently, after training on a self‐built dataset of prefabricated concrete composite panels, ablation experiments and comparative tests were conducted. The GARM segmentation model demonstrated superior performance in terms of detection speed and model lightweighting. Its accuracy surpassed other models, with a mean average precision (mAP50 ) of 88.7%. This study confirms the efficacy and reliability of the GARM instance segmentation model in detecting prefabricated concrete composite panels.
中文翻译:
一种用于预制构件检测的图注意力推理模型
在铸造预制元件之前,准确检查内部组件的位置和是否存在对于确保产品质量至关重要。然而,传统的人工目视检查往往效率低下且不准确。虽然深度学习已广泛应用于预制组件的质量检查,但大多数研究都集中在表面缺陷和裂纹上,而较少强调这些组件的内部结构复杂性。预制复合板由于其结构复杂(包括小型嵌入式部件和大型加强筋),需要高精度、多尺度的特征识别。本研究开发了一种实例分割模型:用于预制构件检测的图注意力推理模型 (GARM),用于预制混凝土组合板的质量检查。首先,构建了预制混凝土复合构件数据集,以解决现有数据的短缺问题,并为训练分割网络提供足够的样本。随后,在对预制混凝土组合板的自建数据集进行训练后,进行了烧蚀实验和对比测试。GARM 分割模型在检测速度和模型轻量化方面表现出卓越的性能。其准确度优于其他型号,平均精密度均值 (mAP50) 为 88.7%。本研究证实了 GARM 实例分割模型在检测预制混凝土组合板方面的有效性和可靠性。
更新日期:2025-01-02
中文翻译:
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一种用于预制构件检测的图注意力推理模型
在铸造预制元件之前,准确检查内部组件的位置和是否存在对于确保产品质量至关重要。然而,传统的人工目视检查往往效率低下且不准确。虽然深度学习已广泛应用于预制组件的质量检查,但大多数研究都集中在表面缺陷和裂纹上,而较少强调这些组件的内部结构复杂性。预制复合板由于其结构复杂(包括小型嵌入式部件和大型加强筋),需要高精度、多尺度的特征识别。本研究开发了一种实例分割模型:用于预制构件检测的图注意力推理模型 (GARM),用于预制混凝土组合板的质量检查。首先,构建了预制混凝土复合构件数据集,以解决现有数据的短缺问题,并为训练分割网络提供足够的样本。随后,在对预制混凝土组合板的自建数据集进行训练后,进行了烧蚀实验和对比测试。GARM 分割模型在检测速度和模型轻量化方面表现出卓越的性能。其准确度优于其他型号,平均精密度均值 (mAP50) 为 88.7%。本研究证实了 GARM 实例分割模型在检测预制混凝土组合板方面的有效性和可靠性。