Detection and Application of Concrete Compressive Strength with Machine Vision Technology

This paper proposes research on concrete compressive strength detection based on machine vision technology in response to the problems of traditional concrete compressive strength detection methods. The original image is directly input into the network for training. There will be too much memory occupation. The efficiency is too low, it needs to be visually preprocessed to meet the requirements of the dataset to establish a high-performance concrete compressive strength detection network model. Then, the processed high-performance concrete compressive strength feature images are inputted into the Mask R-CNN network. The compressive strength features are extracted and trained by the convolutional neural network. Then, the extracted features are further processed with a bilinear difference to ensure their integrity, and finally, the construction of the high-performance concrete detection model based on Mask R-CNN is completed. Simulation experiments are used to analyze the detection and application of high-performance concrete using the theory of machine vision. The data show that the training and validation accuracies of the Mask R-CNN model are 96.75% and 96.52%, respectively, which are 5% and 3.77% higher than that of the migration learning network model (91.75%, 92.75%). In addition, the predictions of the Mask R-CNN model have a relative error of less than 0.05 compared to the actual values. The research presented in this paper is applicable to field strength testing of common concrete structures and can provide theoretical references for the study of construction materials.