scholarly journals Pole Like Object Detection using PCA inTerrestrial LiDAR System

2021 ◽  
Vol 2089 (1) ◽  
pp. 012004
Author(s):  
P Agarwal ◽  
A Husain ◽  
RK Ranjan
Keyword(s):  
Object Detection ◽  
Principal Component ◽  
Threshold Value ◽  
Target Object ◽  
Light Detection And Ranging ◽  
Light Detection ◽  
Detection Approach ◽  
Road Development ◽  
Data Points ◽  
Small Clusters

Abstract Many vertical objects like trees, poles, and pole like structures play a crucial role in the inspection of road safety and planning for road development. The detection of such objects further proves to be helpful in averting roadside accidents and other problems. Light Detection and Ranging technology can be used in identifying these objects. In this paper, we have proposed to detect pole like structures from the dataset generated using a Light Detection and Ranging system. Our proposed pole like object detection approach first segments data into multiple small clusters. The clusters are further analyzed to compute the covariance to identify the linear relationship among the variables. Then eigenvectors and eigenvalues are computed to identify the directions and strength of the data points of clusters. Finally, the Principal Component Analysis approach is used to detect the pole-like structures. The approach is used to identify the target object which uses a threshold value for the angle of the object greater than 70° with respect to the surface. It also uses a normalized eigenvalue equals to 0.7. The efficiency of the proposed is recorded as 93.7%, and the time taken to process the data and detection of the pole-like structures from the dataset is 15 min and 30 sec.

scholarly journals Pengembangan Software Aplikasi Pendeteksian Objek Berbasis LiDAR (Light Detection and Ranging) (Development of Lidar-Based Object Detection Software)

2021 ◽  
Vol 3 ◽  
pp. 73-82
Author(s):  
Raden Aditya Satria Nugraha ◽  
Denden Mohammad Arifin ◽  
Arief Suryadi Satyawan ◽  
Mohammed Ikrom Asysyakuur ◽  
Nafisun Nufus ◽  
...  
Keyword(s):  
Object Detection ◽  
Light Detection And Ranging ◽  
Light Detection ◽  
Detection Software

Mobil adalah sarana transportasi yang kebutuhannya semakin tinggi. Hal ini tidak saja terjadi di luar negeri tapi juga di Indonesia. Namun demikian, keberadaan mobil saat ini dikeluhkan karena polusi yang dihasilkan dan juga tingkat kenyamannya. Harapan di masa mendatang sepertinya lebih mengarah pada hadirnya mobil listrik dengan tingkat polusi sangat rendah, serta kenyamanan dalam penggunaannya, seperti halnya mobil listrik otonom. Di negara maju gagasan ini sudah mulai akan direalisasikan, dan Indonesia sepertinya juga akan menghadapi situasi dimana mobil tersebut menjadi masif digunakan. Oleh sebab itu, kita harus menguasai teknologi kendaraan listrik otonom agar kita dapat memasuki era Mobility in Society 5.0. Salah satu bentuk teknologi terkait adalah sistem software pendeteksian objek berbasis LiDAR. Adakalanya software yang menyertai suatu alat tidak dapat menyediakan fasilitas yang beragam sesuai dengan kebutuhan aplikasi di lapangan. Hal ini dikarenakan keterbatasan yang diberikan oleh produsen alat tersebut, begitu pula dengan produk LiDAR 2D yang banyak dipasaran, contohnya YDLiDAR. Untuk keperluan aplikasi pendeteksian objek, software yang disediakan memiliki keterbatasan dalam hal penyimpanan data, fleksibilitas penyajian data dan kemampuan mereduksi derau yang muncul saat LiDAR tersebut dioperasikan pada kondisi tertentu. Untuk mengatasi kekurangan tersebut diatas, maka pada penelitian ini dikembangkan software pendeteksian objek berbasi LiDAR yang menambahkan fungsi-fungsi tersebut di atas, serta dapat diaplikasikan untuk pendeteksian objek dan pengenalan jarak. Secara umum sistem ini memadukan sistem software yang dikembangkan pada laptop dengan sistem hardware yang terdiri dari YDLiDAR G4 dan interface data serial. Sistem software ini juga dikembangkan dengan menggunakan bahasa pemrograman python. Hasil pengukuran menunjukkan bahwa kinerja software yang dikembangkan memiliki performansi visual yang baik, dapat menyimpan data hasil deteksi dengan durasi yang dapat ditentukan, serta kemampuan dalam menekan derau yang cukup baik. Kemampuan mereduksi noise dari sistem software ini dapat mereduksi error hingga 19,2%.

scholarly journals A robotic grasping approach with elliptical cone-based potential fields under disturbed scenes

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142098573
Author(s):  
Wenjie Geng ◽  
Zhiqiang Cao ◽  
Zhonghui Li ◽  
Yingying Yu ◽  
Fengshui Jing ◽  
...  
Keyword(s):  
Principal Component ◽  
Major Axis ◽  
Target Object ◽  
Potential Fields ◽  
Single Shot ◽  
Robotic Grasping ◽  
Vertical Projection ◽  
Convex Curves ◽  
Functional Representation ◽  
Continuous Potential

Vision-based grasping plays an important role in the robot providing better services. It is still challenging under disturbed scenes, where the target object cannot be directly grasped constrained by the interferences from other objects. In this article, a robotic grasping approach with firstly moving the interference objects is proposed based on elliptical cone-based potential fields. Single-shot multibox detector (SSD) is adopted to detect objects, and considering the scene complexity, Euclidean cluster is also employed to obtain the objects without being trained by SSD. And then, we acquire the vertical projection of the point cloud for each object. Considering that different objects have different shapes with respective orientation, the vertical projection is executed along its major axis acquired by the principal component analysis. On this basis, the minimum projected envelope rectangle of each object is obtained. To construct continuous potential field functions, an elliptical-based functional representation is introduced due to the better matching degree of the ellipse with the envelope rectangle among continuous closed convex curves. Guided by the design principles, including continuity, same-eccentricity equivalence, and monotonicity, the potential fields based on elliptical cone are designed. The current interference object to be grasped generates an attractive field, whereas other objects correspond to repulsive ones, and their resultant field is used to solve the best placement of the current interference object. The effectiveness of the proposed approach is verified by experiments.

scholarly journals Automatic Roadway Features Detection with Oriented Object Detection

2021 ◽  
Vol 11 (8) ◽  
pp. 3531
Author(s):  
Hesham M. Eraqi ◽  
Karim Soliman ◽  
Dalia Said ◽  
Omar R. Elezaby ◽  
Mohamed N. Moustafa ◽  
...  
Keyword(s):  
Object Detection ◽  
Safety Evaluation ◽  
Autonomous Driving ◽  
Detection Accuracy ◽  
The Road ◽  
Detection Model ◽  
Detection Approach ◽  
Roadway Safety ◽  
Safety Features ◽  
Oriented Object

Extensive research efforts have been devoted to identify and improve roadway features that impact safety. Maintaining roadway safety features relies on costly manual operations of regular road surveying and data analysis. This paper introduces an automatic roadway safety features detection approach, which harnesses the potential of artificial intelligence (AI) computer vision to make the process more efficient and less costly. Given a front-facing camera and a global positioning system (GPS) sensor, the proposed system automatically evaluates ten roadway safety features. The system is composed of an oriented (or rotated) object detection model, which solves an orientation encoding discontinuity problem to improve detection accuracy, and a rule-based roadway safety evaluation module. To train and validate the proposed model, a fully-annotated dataset for roadway safety features extraction was collected covering 473 km of roads. The proposed method baseline results are found encouraging when compared to the state-of-the-art models. Different oriented object detection strategies are presented and discussed, and the developed model resulted in improving the mean average precision (mAP) by 16.9% when compared with the literature. The roadway safety feature average prediction accuracy is 84.39% and ranges between 91.11% and 63.12%. The introduced model can pervasively enable/disable autonomous driving (AD) based on safety features of the road; and empower connected vehicles (CV) to send and receive estimated safety features, alerting drivers about black spots or relatively less-safe segments or roads.

scholarly journals A novel bidirectional clustering algorithm based on local density

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Baicheng Lyu ◽  
Wenhua Wu ◽  
Zhiqiang Hu
Keyword(s):  
Clustering Algorithm ◽  
Local Density ◽  
Clustering Algorithms ◽  
Cluster Number ◽  
Denoising Method ◽  
Number Of Clusters ◽  
Data Points ◽  
Cutoff Distance ◽  
Large Clusters ◽  
Small Clusters

AbstractWith the widely application of cluster analysis, the number of clusters is gradually increasing, as is the difficulty in selecting the judgment indicators of cluster numbers. Also, small clusters are crucial to discovering the extreme characteristics of data samples, but current clustering algorithms focus mainly on analyzing large clusters. In this paper, a bidirectional clustering algorithm based on local density (BCALoD) is proposed. BCALoD establishes the connection between data points based on local density, can automatically determine the number of clusters, is more sensitive to small clusters, and can reduce the adjusted parameters to a minimum. On the basis of the robustness of cluster number to noise, a denoising method suitable for BCALoD is proposed. Different cutoff distance and cutoff density are assigned to each data cluster, which results in improved clustering performance. Clustering ability of BCALoD is verified by randomly generated datasets and city light satellite images.

scholarly journals Using Airborne Light Detection and Ranging (LIDAR) to Characterize Forest Stand Condition on the Kenai Peninsula of Alaska

2009 ◽  
Vol 24 (2) ◽  
pp. 95-102 ◽  
Author(s):  
Hans-Erik Andersen
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Grid Cell ◽  
Forest Stand ◽  
Light Detection And Ranging ◽  
Inventory Analysis ◽  
Light Detection ◽  
Individual Tree ◽  
Kenai Peninsula

Abstract Airborne laser scanning (also known as light detection and ranging or LIDAR) data were used to estimate three fundamental forest stand condition classes (forest stand size, land cover type, and canopy closure) at 32 Forest Inventory Analysis (FIA) plots distributed over the Kenai Peninsula of Alaska. Individual tree crown segment attributes (height, area, and species type) were derived from the three-dimensional LIDAR point cloud, LIDAR-based canopy height models, and LIDAR return intensity information. The LIDAR-based crown segment and canopy cover information was then used to estimate condition classes at each 10-m grid cell on a 300 × 300-m area surrounding each FIA plot. A quantitative comparison of the LIDAR- and field-based condition classifications at the subplot centers indicates that LIDAR has potential as a useful sampling tool in an operational forest inventory program.

Precision and shortcomings of yaw error estimation using spinner-based light detection and ranging

Wind Energy ◽  
2012 ◽  
Vol 16 (3) ◽  
pp. 353-366 ◽  
Author(s):  
Knud A. Kragh ◽  
Morten H. Hansen ◽  
Torben Mikkelsen
Keyword(s):  
Error Estimation ◽  
Light Detection And Ranging ◽  
Light Detection
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