Effectiveness of Training Sample and Features for Random Forest on Road Extraction from Unmanned Aerial Vehicle-Based Point Cloud

Biçici, Serkan; Zeybek, Mustafa

doi:10.1177/03611981211029645

Cited by 12 publications

(13 citation statements)

References 52 publications

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“…Based on the above points, it is extremely urgent to find a convenient and effective means of rehabilitation training to enable patients to recover their active sports ability, improve their quality of life, and reduce the burden on families and society. With the continuous progress of science and technology and the in-depth application of automated robots, the field of rehabilitation medicine is gradually using robots to train patients with motor dysfunction, which can significantly improve the loss of motor function caused by nervous system damage (such as stroke, trauma, and spinal injury), as shown in Figure 1 [2]. Using robot technology to evaluate, reconstruct, and improve the movement ability of patients' limbs has become a hot topic in domestic research.…”

Section: Introductionmentioning

confidence: 99%

Motion Rehabilitation Robot Control Based on Human Posture Information

He¹

2022

Journal of Sensors

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In order to meet the needs of postoperative rehabilitation training of lower limbs, a motion rehabilitation robot control system based on human posture information is proposed in this paper. The functions of active/passive training mode control, movement posture and EMG signal acquisition, WiFi communication, safety protection, etc. of the lower limb rehabilitation robot are realized. The recognition and analysis of the training process are realized by using random forest machine learning algorithm and linear regression algorithm. The experimental results show that in the first row of the confusion matrix of the random forest algorithm, 7316 data are correctly identified as speed a and only one data is incorrectly identified as speed B , which is superior to other algorithms. In conclusion, the developed control and monitoring system of lower limb rehabilitation robot can be portable controlled by Android and can realize intelligent analysis of the training process through the monitoring signals in the training process. At the same time, the random forest algorithm has more advantages than the linear regression algorithm in motion recognition, which is of positive significance to the automatic monitoring and intelligent control of the training process.

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Section: Introductionmentioning

confidence: 99%

Motion Rehabilitation Robot Control Based on Human Posture Information

He¹

2022

Journal of Sensors

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“…Recent advancements in positioning and imaging sensors, computer software and hardware, and particularly, image-based 3D reconstruction algorithms have sparked growing interest in the development of low-cost MMS based on photogrammetric techniques [ 12 , 13 ]. In addition, the decreasing costs of imaging sensors has made photogrammetry-based MMS systems an extremely attractive alternative to LiDAR-based systems in terms of cost and easier-to-operate logistics [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…The point density of the LiDAR-acquired point cloud is defined in angular resolution, and thus, the density on object surfaces declines with the object distance at the rate of 1/range 2 . Therefore, from the various LiDAR platforms, mobile laser scanning systems (MLS), both vehicle and UAS platforms, are considered the primary technology for acquiring adequate 3D point cloud data to support road-related information extraction, see relevant studies [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

3D Road Boundary Extraction Based on Machine Learning Strategy Using LiDAR and Image-Derived MMS Point Clouds

Suleymanoglu,

Soycan,

Toth

2024

Sensors

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The precise extraction of road boundaries is an essential task to obtain road infrastructure data that can support various applications, such as maintenance, autonomous driving, vehicle navigation, and the generation of high-definition maps (HD map). Despite promising outcomes in prior studies, challenges persist in road extraction, particularly in discerning diverse road types. The proposed methodology integrates state-of-the-art techniques like DBSCAN and RANSAC, aiming to establish a universally applicable approach for diverse mobile mapping systems. This effort represents a pioneering step in extracting road information from image-based point cloud data. To assess the efficacy of the proposed method, we conducted experiments using a large-scale dataset acquired by two mobile mapping systems on the Yıldız Technical University campus; one system was configured as a mobile LiDAR system (MLS), while the other was equipped with cameras to operate as a photogrammetry-based mobile mapping system (MMS). Using manually measured reference road boundary data, we evaluated the completeness, correctness, and quality parameters of the road extraction performance of our proposed method based on two datasets. The completeness rates were 93.2% and 84.5%, while the correctness rates were 98.6% and 93.6%, respectively. The overall quality of the road curb extraction was 93.9% and 84.5% for the two datasets. Our proposed algorithm is capable of accurately extracting straight or curved road boundaries and curbs from complex point cloud data that includes vehicles, pedestrians, and other obstacles in urban environment. Furthermore, our experiments demonstrate that the algorithm can be applied to point cloud data acquired from different systems, such as MLS and MMS, with varying spatial resolutions and accuracy levels.

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“…As an emerging data collection platform, UAVs have the characteristics of small size [16], light weight [17], and fexibility [18,19] and have been widely used in geographic mapping [20], disaster assessment [21,22], and archaeological exploration [23,24]. At present, UAVs are also widely used in the study of traditional villages, e.g., Sestras et al [25] used drone image data and 3D reconstruction technology for ancient building restoration and tourism potential analysis.…”

Section: Introductionmentioning

confidence: 99%

Extraction and Analysis of Spatial Feature Data of Traditional Villages Based on the Unmanned Aerial Vehicle (UAV) Image

Teng

Chun-tao

Zhao

et al. 2022

Mobile Information Systems

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In the process of the evolution of urban-rural spatial patterns, traditional village forms have been seriously eroded and destroyed, resulting in the gradual loss of garden feature elements and the blurring of spatial features. Facing these problems, accurate and efficient acquisition of relevant data becomes the key to traditional village conservation. We take the typical Huizhou Shuikou garden Tangmo village as an example and conduct a study on how to use UAV aerial images to extract spatial feature data. Firstly, the UAV was used to acquire aerial images of the study area through a predefined mission. Then, the image data are processed by using structure from motion (SfM) software to obtain digital surface model (DSM), digital orthophoto map (DOM), point cloud, and 3D model. Finally, the produced spatial data are applied to the spatial feature extraction analysis study. The results show the following: (1) the data produced by UAV aerial images have a horizontal accuracy of 0.034 m and a vertical accuracy of 0.039 m, which meet the requirements of traditional village spatial data collection. (2) The results of spatial feature elements and terrain feature extraction show that the 3D model and DSM can accurately extract and analyze the micro and macro spatial feature of traditional villages. (3) By using the cloth simulation filtering (CSF) to extract point clouds of buildings and streets, and after statistical analysis, we could quickly obtain their spatial features. Based on the spatial data obtained by using UAV, the study achieves the accurate collection of Tangmo DSM, DOM, 3D model, and spatial feature data and forms a method of data collection, processing and spatial feature extraction. The results of the study can provide a scientific basis for data collection on the spatial features of other cultural heritages and the conservation of traditional villages.

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Effectiveness of Training Sample and Features for Random Forest on Road Extraction from Unmanned Aerial Vehicle-Based Point Cloud

Cited by 12 publications

References 52 publications

Motion Rehabilitation Robot Control Based on Human Posture Information

Motion Rehabilitation Robot Control Based on Human Posture Information

3D Road Boundary Extraction Based on Machine Learning Strategy Using LiDAR and Image-Derived MMS Point Clouds

Extraction and Analysis of Spatial Feature Data of Traditional Villages Based on the Unmanned Aerial Vehicle (UAV) Image

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