Coded target recognition algorithm for vision measurement

Circularly coded targets are widely used in 3D measurement, target tracking, augmented reality, and other fields as feature points to be measured. The traditional coded target recognition algorithm is easily affected by illumination changes and excessive shooting angles, and the recognition accuracy is significantly reduced. Therefore, a new coded target recognition algorithm is required to reduce the effects of illumination and angle on the recognition process. The influence of illumination on the recognition of coding targets was analyzed in depth, and the advantages and disadvantages of traditional algorithms are discussed. A new adaptive threshold image segmentation method was designed, which, in contrast to traditional algorithms, incorporates the feature information of coding targets in the determination of the image segmentation threshold. The experimental results show that this method significantly reduces the influence of illumination variations and cluttered backgrounds on image segmentation. Similarly, the influence of different angles on the recognition process of coding targets was studied. The coding target is decoded by radial sampling of the dense point network, which can effectively reduce the influence of angle on the recognition process and improve the recognition accuracy of coding targets and the robustness of the algorithm. In addition, further experiments verified that the proposed detection and recognition algorithm can better extract and identify with high positioning accuracy and decoding success rate. It can achieve accurate positioning even in complex environments and meet the needs of industrial measurements.