Measurement method of a monocular visual ramming settlement based on the characteristics of the rammer handle

Abstract

Ramming settlement is a key indicator to measure the quality of dynamic compaction reinforcement. Relying on manual measurement is not only costly and inefficient, but also cannot guarantee the safety of personnel. This paper proposes a monocular vision non-contact ramming settlement measurement method, aiming to improve construction efficiency, and this method can be carried out simultaneously with construction. Through the modeling of the vision camera, combined with deep learning and Fourier transform method, the rammer feature point information is extracted, and the calculation model of ramming settlement is constructed. Through the error fitting analysis, the influencing factors of ramming settlement detection are comprehensively studied. Experiments show that under real compaction conditions, the accuracy reaches 30 mm, and the accuracy that meets construction requirements reaches 91.28%. It has the characteristics of low cost, high precision and strong stability, and can be effectively applied to the automatic calculation of ramming settlement.