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Ecological retaining wall building block and intelligent preparation method thereof
The invention discloses an ecological retaining wall building block and an intelligent preparation method thereof. The method comprises the following steps: firstly, carrying out binarization processing on an X-Ray image of a detected ecological retaining wall building block to obtain a binarized X-Ray image, then, extracting a histogram of oriented gradient of the binarized X-Ray image, then, carrying out aggregation on the images, carrying out image partitioning processing, then, obtaining context semantic feature vectors of a plurality of image blocks through a ViT model, and finally, carrying out image segmentation processing on the context semantic feature vectors of the plurality of image blocks through the ViT model. Cascading the context semantic feature vectors of the plurality of image blocks to obtain a global semantic feature vector, then arranging the context semantic feature vectors of the plurality of image blocks into a two-dimensional feature matrix, then obtaining a local enhanced feature vector through a convolutional neural network model, and finally obtaining a local enhanced feature vector through the convolutional neural network model. And enabling a classification feature vector obtained by fusing the global semantic feature vector and the local enhanced feature vector to pass through a classifier to obtain a classification result used for representing whether the detected ecological retaining wall building block has the internal structure defect or not. Therefore, the product quality can be ensured.
公开了一种生态挡土墙砌块及其智能化制备方法。其首先对探测生态挡土墙砌块的X‑Ray图像进行二值化处理以得到二值化X‑Ray图像，接着，提取所述二值化X‑Ray图像的方向梯度直方图，然后，将上述图像进行聚合并进行图像分块处理后通过ViT模型以得到多个图像块上下文语义特征向量，接着，将所述多个图像块上下文语义特征向量进行级联以得到全局语义特征向量，然后，将所述多个图像块上下文语义特征向量排列为二维特征矩阵后通过卷积神经网络模型以得到局部增强特征向量，最后，将融合所述全局语义特征向量和所述局部增强特征向量得到的分类特征向量通过分类器以得到用于表示被探测生态挡土墙砌块是否存在内部结构缺陷的分类结果。这样，可以确保产品质量。
Ecological retaining wall building block and intelligent preparation method thereof
The invention discloses an ecological retaining wall building block and an intelligent preparation method thereof. The method comprises the following steps: firstly, carrying out binarization processing on an X-Ray image of a detected ecological retaining wall building block to obtain a binarized X-Ray image, then, extracting a histogram of oriented gradient of the binarized X-Ray image, then, carrying out aggregation on the images, carrying out image partitioning processing, then, obtaining context semantic feature vectors of a plurality of image blocks through a ViT model, and finally, carrying out image segmentation processing on the context semantic feature vectors of the plurality of image blocks through the ViT model. Cascading the context semantic feature vectors of the plurality of image blocks to obtain a global semantic feature vector, then arranging the context semantic feature vectors of the plurality of image blocks into a two-dimensional feature matrix, then obtaining a local enhanced feature vector through a convolutional neural network model, and finally obtaining a local enhanced feature vector through the convolutional neural network model. And enabling a classification feature vector obtained by fusing the global semantic feature vector and the local enhanced feature vector to pass through a classifier to obtain a classification result used for representing whether the detected ecological retaining wall building block has the internal structure defect or not. Therefore, the product quality can be ensured.
公开了一种生态挡土墙砌块及其智能化制备方法。其首先对探测生态挡土墙砌块的X‑Ray图像进行二值化处理以得到二值化X‑Ray图像，接着，提取所述二值化X‑Ray图像的方向梯度直方图，然后，将上述图像进行聚合并进行图像分块处理后通过ViT模型以得到多个图像块上下文语义特征向量，接着，将所述多个图像块上下文语义特征向量进行级联以得到全局语义特征向量，然后，将所述多个图像块上下文语义特征向量排列为二维特征矩阵后通过卷积神经网络模型以得到局部增强特征向量，最后，将融合所述全局语义特征向量和所述局部增强特征向量得到的分类特征向量通过分类器以得到用于表示被探测生态挡土墙砌块是否存在内部结构缺陷的分类结果。这样，可以确保产品质量。
Ecological retaining wall building block and intelligent preparation method thereof
生态挡土墙砌块及其智能化制备方法
XIAO TIEJUN (author) / PENG TIANLI (author) / ZHU LEHENG (author)
2023-07-28
Patent
Electronic Resource
Chinese
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