Image-Based Precision Measurement Technology for the Quality Inspection of Crane Boom Materials

Abstract

With the slow rise of the construction industry, cranes, as indispensable mechanical equipment in construction projects, are widely used in the lifting and handling of specific space ranges of construction projects. The quality of crane booms is particularly important for safety. This paper uses image measurement methods including filter processing, mean filtering, and Gaussian filtering to detect the quality of the crane boom material. The image is processed by the wavelet transform and Fourier transform. The grayscale transformation stretching method is applied to the surface image analysis of the boom material to obtain the final inspection. The research results show that the use of image measurement methods can effectively measure the thickness of the crane boom material, the geometric information of the boom material, and the surface roughness of the material and obtain effective image information. The detection accuracy reaches 98.1%. The error can be controlled better. The inspection and research on the quality of crane jib materials can ensure the quality and performance of crane jib materials, reduce the potential safety hazards of cranes during operation, and improve safety. This article organically combines workpiece surface roughness detection with digital image processing technology to preprocess the surface picture of the arm tube material. On this basis, the texture features in the image are extracted and programmed to calculate, and the final workpiece is obtained by the surface roughness value, which proves the feasibility of this method. The research results have very important practical significance for the detection of the quality of the arm tube material and the improvement of the quality level of the arm tube material.