Research on automatic detection and evaluation algorithm of assembled pier construction quality based on machine vision

As a key component of bridge structure, the construction quality of assembled pier is very important to the safety and service life of bridge. Traditional detection methods, such as manual visual inspection and physical measurement, are time-consuming, laborious and subjective. This study uses machine vision technology, combined with computer science, image processing, pattern recognition and artificial intelligence to obtain the image and physical parameters of pier in real time through industrial cameras and sensor networks, and carries out data acquisition, preprocessing, feature extraction, quality detection and evaluation through software modules. The system adopts support vector machine (SVM) as the quality detection model, and through the learning and feature analysis of a large number of image samples, the automatic detection and accurate evaluation of key quality indicators such as pier surface defects, size deviation and connection quality are realized. The experimental results show that the accuracy of SVM model in identifying different quality states of piers is higher than that of traditional detection methods, showing good robustness and generalization ability. The system design and algorithm research in this study provide an efficient and accurate solution for the automatic detection of the construction quality of assembled piers, which has important engineering application value and broad market prospects.