Nowadays, the technology of using computer vision to calibrate objects is widely used, which has a huge market demand in many fields. This paper provides a calibration method of computer vision system based 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 the minimum. 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.

如今，使用计算机视觉来校准物体的技术已被广泛使用，这在许多领域都具有巨大的市场需求。本文提出了一种基于双关注神经网络的计算机视觉系统的标定方法。本文使用相机模拟人眼以获得三维图像。在获得三维图像后，将图像输入到残差网络（ResNet）模型中，并反复更新ResNet的权重以准确识别图像。在此基础上，引入了双重关注机制，即在视觉图像处理中采用自然语言算法，采用多阶段特征提取方法针对区域的每个特征提取三维图像。提取特征区域后，特征区域的精度会不断更新到最低。此外，将特征区域引入张正有系统的标定算法中，以获取关注区域中物体的空间坐标。该方法可以实现各种复杂背景下特定物体的空间位置校准，并计算出与被校准物体的距离，不仅可以对系统进行校准，还可以对其进行识别，从而大大提高了校准过程的可靠性和准确性。