Surface detection method of glass fibre composites based on computer vision

Abstract

Considering the high cost, low efficiency and poor real-time performance of manual inspection methods in detecting surface defects such as glass fibre imprints, resin build-up and wrinkles, in this paper, a machine vision-based method is proposed to detect surface defects of glass fibre composites. The method designs an automatic inspection platform using two high-resolution line scan cameras for image acquisition. The eight directional templates of the Kirsch operator are used to convolve the derivatives of the image pixel points respectively, and the largest template is selected to determine its edge direction, and the detection of surface defects is achieved by combining with the Canny operator. The experimental results show that the proposed algorithm can well suppress noise interference, improve the accuracy of edge localisation and detection, and well retain edge information while avoiding pseudo-edges.