Purpose

Laser-based measurement techniques offer various advantages over conventional measurement techniques, such as no-destructive, no-contact, fast and long measuring distance. In cooperative laser ranging systems, it’s crucial to extract center coordinates of retroreflectors to accomplish automatic measurement. To solve this problem, this paper aims to propose a novel method.

Design/methodology/approach

We propose a method using Mask RCNN (Region Convolutional Neural Network), with ResNet101 (Residual Network 101) and FPN (Feature Pyramid Network) as the backbone, to localize retroreflectors, realizing automatic recognition in different backgrounds. Compared with two other deep learning algorithms, experiments show that the recognition rate of Mask RCNN is better especially for small-scale targets. Based on this, an ellipse detection algorithm is introduced to obtain the ellipses of retroreflectors from recognized target areas. The center coordinates of retroreflectors in the camera coordinate system are obtained by using a mathematics method.

Findings

To verify the accuracy of this method, an experiment was carried out: the distance between two retroreflectors with a known distance of 1,000.109 mm was measured, with 2.596 mm root-mean-squar error, meeting the requirements of the coarse location of retroreflectors.

Research limitations/implications

The research limitations/implications are as follows: (i) As the data set only has 200 pictures, although we have used some data augmentation methods such as rotating, mirroring and cropping, there is still room for improvement in the generalization ability of detection. (ii) The ellipse detection algorithm needs to work in relatively dark conditions, as the retroreflector is made of stainless steel, which easily reflects light.

Originality/value

The originality/value of the article lies in being able to obtain center coordinates of multiple retroreflectors automatically even in a cluttered background; being able to recognize retroreflectors with different sizes, especially for small targets; meeting the recognition requirement of multiple targets in a large field of view and obtaining 3 D centers of targets by monocular model-based vision.

中文翻译：

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一种基于深度学习的反射镜3维中心定位方法

目的

基于激光的测量技术与传统测量技术相比具有多种优势，例如无损、非接触、快速和长测量距离。在协同激光测距系统中，提取后向反射器的中心坐标以完成自动测量至关重要。为了解决这个问题，本文旨在提出一种新的方法。

设计/方法/方法

我们提出了一种使用Mask RCNN（区域卷积神经网络），以ResNet101（残差网络101）和FPN（特征金字塔网络）为骨干的方法来定位后向反射器，实现不同背景下的自动识别。与其他两种深度学习算法相比，实验表明Mask RCNN的识别率更好，尤其是对小规模目标。在此基础上，引入椭圆检测算法，从识别的目标区域中获取回射器的椭圆。使用数学方法获得相机坐标系中回射器的中心坐标。

发现

为了验证该方法的准确性，进行了实验：测量了两个已知距离为1,000.109 mm的后向反射器之间的距离，均方根误差为2.596 mm，满足后向反射器粗定位的要求。

研究限制/影响

研究局限性/意义如下： (i) 由于数据集只有200张图片，虽然我们使用了一些数据增强方法，如旋转、镜像和裁剪，但在检测的泛化能力上仍有提升空间。(ii) 椭圆检测算法需要在相对较暗的条件下工作，因为后向反射器由不锈钢制成，容易反射光线。

原创性/价值

文章的独创性/价值在于即使在杂乱的背景下也能自动获取多个回射器的中心坐标；能够识别不同尺寸的后向反射器，特别是对于小目标；满足大视场内多目标的识别需求，通过单目模型视觉获取目标的3D中心。