A machine vision approach with temporal fusion strategy for concrete vibration quality monitoring

Abstract

In concrete construction, ensuring the quality of vibration is paramount for maintaining the strength, durability, and quality of structures. This study proposed a method for monitoring the vibration quality of vibrating robots to replace subjective human judgment. The method employed an improved EfficientNetV2 model to classify the vibration quality into four levels: unqualified, middle, qualified, and over-vibration. A temporal fusion strategy was introduced, employing time-domain probability fusion to enhance the accuracy and stability of the results. Additionally, image patching was applied to reduce computational complexity while preserving feature integrity. Experimental results demonstrate that the proposed method outperforms common mainstream models, achieving an accuracy of 96.47%, with a relatively small parameter size of 13.8 M. Compared to non-temporal fusion strategy, the accuracy is improved by 2.06%. This research has been successfully applied in practical engineering, providing a reliable means of quality assurance for concrete structures and demonstrating potential application prospects in the field of engineering construction.