A calibration method of computer vision system based on dual attention mechanism

Nowadays, the technology of using computer vision to calibrate objects iswidely used, which has a hugemarket demand in many fields. This paper provides a calibration method of computer vision systembased on dual attention neural network. This paper uses the camera to simulate human eyes to obtain three-dimensional images. After obtaining the three-dimensional images, the images are input into the Residual Network (ResNet) model, and the weight of ResNet is repeatedly updated so as to accurately identify the images. On this basis, introduces dual attention mechanism that an algorithm is used in natural language to the visual image processing, using multistage feature extraction method to extract the three-dimensional image for each characteristic of regional. After extracting the feature area, the accuracy of the feature area is constantly updated to theminimum. Besides, the feature areas are brought into the calibration algorithm of Zhang Zhengyou system to obtain the spatial coordinates of the objects in the attention area. This method can realize the space position calibration of specific objects under various complex backgrounds and calculate the distance from the calibrated objects, which can not only calibrate the system but also identify it, and greatly improve the reliability and accuracy of the calibration process. (c) 2020 Elsevier B.V. All rights reserved.