Precise control mode for concrete vibration time based on attention-enhanced machine vision

Abstract

In concrete construction, vibration is an essential process used to ensure the structural soundness and long-term durability of concrete structures. Automating concrete vibration monitoring enables far more precise control compared to subjective human judgments. In this paper, a control mode for concrete vibration time used on the vibrating robot is proposed. The control mode utilizes YOLOv8 model to recognize the best-vibrating position and eliminate the rebar part of concrete surface image and employs attention-enhanced squeeze-and-excitation neural network to regress the vibrating completion degree. After deployment to the embedded system of vibrating robot, experimental results demonstrate superior performance of the proposed method over state-of-the-art algorithms with the highest recognition rate and maximum R2 value. The control mode enhances vibration quality while liberating workers from manual tasks. A commercial mall construction project validates the practicality of the presented control model.