一种应用于复杂场景的线结构光激光中心线提取方法

线结构光激光中心线提取是线结构光三维测量的关键技术之一,其提取精度对系统测量精度有决定性影响。实际三维测量中,相机采集图像受物体表面反射率等因素的影响,给激光中心线提取增加了难度,针对这一问题,提出了一种结合灰度重心法的改进提取方法。该方法基于连通域分析去除背景光条和噪声区域,接着基于线宽分布实现局部形态学处理,最后结合灰度重心法计算出激光中心线的亚像素位置。该方法的中心线计算精度相较于改进前提高了约60%,在条纹图像出现过曝光和欠曝光区域仍能比较准确提取激光中心线的位置,可以适应复杂环境的考验,能够保证激光中心线提取工作的稳定性和精确性。

Sub-pixel extraction of laser stripeand itsapplication in laser plane calibration

For calibrating the laser plane to implement 3D shape measurement, an algorithm for extracting the laser stripe with sub-pixel accuracy is proposed. The proposed algorithm mainly consists of two stages： two-side edge detection and center line extraction. First, the two-side edge of laser stripe is detected using the principal component angle-based progressive probabilistic Hough transform and its width is calculated through the distance between these two edges. Secondly, the center line of laser strip is extracted with 2D Taylor expansion at a sub-pixel level and the laser plane is calibrated with the 3D reconstructed coordinates from the extracted 2D sub-pixel ones. Experimental results demonstrate that the proposed method can not only extract the laser stripe at a high speed, nearly average 78 ms/frame, but also calibrate the coplanar laser stripes at a low error, limited to 0.3 mm. The proposed algorithm can satisfy the system requirement of two-side edge detection and center line extraction, and rapid speed, high precision, as well as strong anti-jamming.

基于RGB-D数据的笔记本面板表面缺陷检测研究

笔记本面板表面的鼓包、麻点等缺陷在RGB图像上特征不显著,而细小划痕缺陷在深度相机上又无法采集其深度信息。为了同时满足深度空间和颜色空间这两类缺陷的特征采集,搭建了RGB-D数据采集平台,采用线激光三角测量法和错位取值的方法,通过一台相机和线激光发射器同时采集RGB图像和深度图像。提取激光中心线时通过对激光图像的两个通道做差,消除外部光源的干扰,提高了激光中心线的提取精度。将采集到的RGB-D数据进行图像处理后制作成数据集,采用基于Swin Transformer优化的Mask RCNN模型对数据集进行训练和验证。结果表明,该数据采集平台能实现鼓包、麻点、划痕等多种缺陷类型的缺陷特征采集,并通过深度学习的方法进行缺陷检测。改进后的Mask RCNN网络模型平均检测精度m AP达到86.5%,比传统基于ResNet50网络的Mask RCNN精度提升9.5%,在大目标缺陷上检测精度AP提升了13.3%。该方案能够有效降低漏检率,满足对笔记本面板表面的缺陷检测要求。