Abstract
This study developed an algorithm capable of detecting a reference line (a 0.2 mm thick piano wire) to accurately determine the position of an automated installation robot within an elevator shaft. A total of 3,245 images were collected from the experimental tower of H Company, the leading elevator manufacturer in South Korea, and the detection performance was evaluated using four experimental approaches (GCH, GSCH, GECH, FCH). During the initial image processing stage, Gaussian blurring, sharpening filter, embossing filter, and Fourier Transform were applied, followed by Canny Edge Detection and Hough Transform. Notably, the method was developed to accurately extract the reference line by averaging the x-coordinates of the lines detected through the Hough Transform. This approach enabled the detection of the 0.2 mm thick piano wire with high accuracy, even in the presence of noise and other interfering factors (e.g., concrete cracks inside the elevator shaft or safety bars for filming equipment). The experimental results showed that Experiment 4 (FCH), which utilized Fourier Transform in the preprocessing stage, achieved the highest detection rate for the LtoL, LtoR, and RtoL datasets. Experiment 2(GSCH), which applied Gaussian blurring and a sharpening filter, demonstrated superior detection performance on the RtoR dataset. This study proposes a reference line detection algorithm that enables precise position calculation and control of automated robots in elevator shaft installation. Moreover, the developed method shows potential for applicability even in confined working spaces. Future work aims to develop a line detection algorithm equipped with machine learning-based hyperparameter tuning capabilities.
Abstract (translated)
这项研究开发了一种能够检测基准线（一根0.2毫米厚的钢琴弦）的算法，以准确确定电梯井中自动安装机器人位置。总共从韩国领先的电梯制造商H公司的实验塔楼收集了3,245张图像，并通过四种实验方法（GCH、GSCH、GECH和FCH）评估了检测性能。在初始图像处理阶段，采用了高斯模糊、锐化滤镜、浮雕滤镜以及傅里叶变换，并随后应用Canny边缘检测和霍夫变换。 特别值得注意的是，该方法通过平均霍夫变换中检测到的线段的x坐标来准确提取基准线。这种方法能够在存在噪声和其他干扰因素（如电梯井内的混凝土裂缝或用于拍摄设备的安全栏杆）的情况下，以高精度识别出0.2毫米厚的钢琴弦。实验结果显示，在预处理阶段使用傅里叶变换的实验4（FCH），在LtoL、LtoR和RtoL数据集上的检测率最高；而应用了高斯模糊与锐化滤镜的实验2（GSCH）则在RtoR数据集中表现出优越的检测性能。 本研究提出了一种基准线检测算法，可以实现电梯井安装中自动机器人精确位置计算和控制。此外，所开发的方法还显示出了适用于狭小工作空间的应用潜力。未来的研究将致力于开发一种具备基于机器学习的超参数调优能力的线条检测算法。
URL
https://arxiv.org/abs/2503.13473
