目的:提高激光测距设备制造过程中装配工艺精度.方法:提出一种基于MobileNeXt和多尺度区域分割的激光对位检测算法用于优化激光测距设备的生产过程.在对位检测阶段,使用MobileNeXt作为主干网络达到低成本计算图像特征的目的.为提升模型捕获有效特征的能力,在主干网络中插入深度可分离坐标注意力模块.通过知识进化训练网络得到轻量高精度的分类模型.在验证阶段,设计一种多尺度区域分割的激光中心对位验证算法.首先通过线性变换和双边滤波处理原始图像,然后以多个尺度分割图像区域,在不同区域进行特征提取.最后根据激光对位特征设计阈值判断对位结果的正确性.结果:算法中心对位检测准确度达到99.97%,对位误差为37.96μm.结论:所提算法满足实际激光对位检测任务要求.
Aims:This paper aims to improve the assembly process accuracy in the manufacturing process of laser ranging equipment.Methods:A laser alignment detection algorithm was proposed based on MobileNeXt and multi-scale region segmentation to optimize the production process of laser ranging equipment.In the alignment detection stage,MobileNeXt was used as the backbone network to compute image features at low cost.And depth separable coordinate attention was introduced into the backbone network to enhance the model's ability to capture effective features.The network was trained using a knowledge evolution training scheme to obtain a lightweight and high-precision classification model.In the validation stage,a laser center alignment verification algorithm with multi-scale region segmentation was designed.The algorithm first processed the original image through linear transformation and bilateral filtering.It then segmented the image into multiple scales and extracted features in different regions.Finally,a threshold was designed based on the laser alignment feature to verify the correctness of the alignment result.Results:The algorithm achieved a center alignment detection accuracy of 99.97%and an alignment error of 37.96 μm.Conclusions:The proposed algorithm meets the requirements of practical laser alignment detection tasks.