Some research issues in hash function learning

Zhen, Yi

doi:10.14711/thesis-b1198629

Cited by 1 publication

(1 citation statement)

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“…In this study, the convolutional layers all have four kernel filters of kernel size 2 × 2 and a step size of one with no padding around the input data. This activation function is the rectified linear unit (ReLU) [8] . Pooling reduces the size mapping of the features by about half.…”

Section: Image Classification Modelmentioning

confidence: 99%

Numerical Detection of Concrete Slump by Fusion of Target Segmentation and Image Classification Network

Gao

Yan

2023

J. Phys.: Conf. Ser.

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In construction, concrete compatibility is an important comprehensive index to ensure the construction, and the concrete slump is an important criterion to judge concrete compatibility in the actual construction process. In this study, we propose to extract new data sets from concrete mixing video sequences and correlate the image features characterized in the concrete mixing and transportation process with the concrete performance features, starting from the concrete transportation process [1]. First, the UNet network model for semantic segmentation is used to identify and locate the concrete regions, and the localized concrete regions are zoomed in using interpolation; then the ResNet image classification network is used to determine the slump category of the processed concrete regions; finally, the results of the semantic segmentation network and the image classification network are fused to obtain the final concrete slump detection results. The experimental results demonstrate that the proposed method can guarantee real-time concrete slump detection with improved accuracy.

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Section: Image Classification Modelmentioning

confidence: 99%