scholarly journals A Multi-Feature Search Window Method for Road Boundary Detection Based on LIDAR Data

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1551 ◽  
Author(s):  
Kai Li ◽  
Jinju Shao ◽  
Dong Guo
Keyword(s):  
Boundary Detection ◽  
Detection Algorithm ◽  
Boundary Extraction ◽  
Cloud Data ◽  
The Road ◽  
Window Method ◽  
Single Feature ◽  
Distribution Features ◽  
Curve Model ◽  
Cluster Points

In order to improve the accuracy of structured road boundary detection and solve the problem of the poor robustness of single feature boundary extraction, this paper proposes a multi-feature road boundary detection algorithm based on HDL-32E LIDAR. According to the road environment and sensor information, the former scenic cloud data is extracted, and the primary and secondary search windows are set according to the road geometric features and the point cloud spatial distribution features. In the search process, we propose the concept of the largest and smallest cluster points set and a two-way search method. Finally, the quadratic curve model is used to fit the road boundary. In the actual road test in the campus road, the accuracy of the linear boundary detection is 97.54%, the accuracy of the curve boundary detection is 92.56%, and the average detection period is 41.8 ms. In addition, the algorithm is still robust in a typical complex road environment.

scholarly journals AN AUTOMATED ROAD ROUGHNESS DETECTION FROM MOBILE LASER SCANNING DATA

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 91-96 ◽  
Author(s):  
P. Kumar ◽  
E. Angelats
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Detection Algorithm ◽  
Road Surface ◽  
Spatial Density ◽  
Point Cloud Data ◽  
Cloud Data ◽  
The Road ◽  
Road Users ◽  
Road Roughness

Rough roads influence the safety of the road users as accident rate increases with increasing unevenness of the road surface. Road roughness regions are required to be efficiently detected and located in order to ensure their maintenance. Mobile Laser Scanning (MLS) systems provide a rapid and cost-effective alternative by providing accurate and dense point cloud data along route corridor. In this paper, an automated algorithm is presented for detecting road roughness from MLS data. The presented algorithm is based on interpolating smooth intensity raster surface from LiDAR point cloud data using point thinning process. The interpolated surface is further processed using morphological and multi-level Otsu thresholding operations to identify candidate road roughness regions. The candidate regions are finally filtered based on spatial density and standard deviation of elevation criteria to detect the roughness along the road surface. The test results of road roughness detection algorithm on two road sections are presented. The developed approach can be used to provide comprehensive information to road authorities in order to schedule maintenance and ensure maximum safety conditions for road users.

scholarly journals A Set of Single YOLO Modalities to Detect Occluded Entities via Viewpoint Conversion

2021 ◽  
Vol 11 (13) ◽  
pp. 6016
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Cloud Data ◽  
Detection Techniques ◽  
Bounding Boxes ◽  
Partially Occluded ◽  
Rgb Image

For autonomous vehicles, it is critical to be aware of the driving environment to avoid collisions and drive safely. The recent evolution of convolutional neural networks has contributed significantly to accelerating the development of object detection techniques that enable autonomous vehicles to handle rapid changes in various driving environments. However, collisions in an autonomous driving environment can still occur due to undetected obstacles and various perception problems, particularly occlusion. Thus, we propose a robust object detection algorithm for environments in which objects are truncated or occluded by employing RGB image and light detection and ranging (LiDAR) bird’s eye view (BEV) representations. This structure combines independent detection results obtained in parallel through “you only look once” networks using an RGB image and a height map converted from the BEV representations of LiDAR’s point cloud data (PCD). The region proposal of an object is determined via non-maximum suppression, which suppresses the bounding boxes of adjacent regions. A performance evaluation of the proposed scheme was performed using the KITTI vision benchmark suite dataset. The results demonstrate the detection accuracy in the case of integration of PCD BEV representations is superior to when only an RGB camera is used. In addition, robustness is improved by significantly enhancing detection accuracy even when the target objects are partially occluded when viewed from the front, which demonstrates that the proposed algorithm outperforms the conventional RGB-based model.

scholarly journals Emergency Landing Spot Detection Algorithm for Unmanned Aerial Vehicles

2021 ◽  
Vol 13 (10) ◽  
pp. 1930
Author(s):  
Gabriel Loureiro ◽  
André Dias ◽  
Alfredo Martins ◽  
José Almeida
Keyword(s):  
Point Cloud ◽  
Principal Component ◽  
Detection Algorithm ◽  
Cloud Data ◽  
Safety Issues ◽  
Spot Detection ◽  
Cloud Clusters ◽  
Aerial Vehicle ◽  
Incoming Light ◽  
Landing Spot

The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance, and others. Considering the increased presence of these vehicles in the airspace, it becomes necessary to reflect on the safety issues or failures that the UAVs may have and the appropriate action. Moreover, in many missions, the vehicle will not return to its original location. If it fails to arrive at the landing spot, it needs to have the onboard capability to estimate the best area to safely land. This paper addresses the scenario of detecting a safe landing spot during operation. The algorithm classifies the incoming Light Detection and Ranging (LiDAR) data and store the location of suitable areas. The developed method analyses geometric features on point cloud data and detects potential right spots. The algorithm uses the Principal Component Analysis (PCA) to find planes in point cloud clusters. The areas that have a slope less than a threshold are considered potential landing spots. These spots are evaluated regarding ground and vehicle conditions such as the distance to the UAV, the presence of obstacles, the area’s roughness, and the spot’s slope. Finally, the output of the algorithm is the optimum spot to land and can vary during operation. The proposed approach evaluates the algorithm in simulated scenarios and an experimental dataset presenting suitability to be applied in real-time operations.

FPGA and ASIC implementation of the algorithm for traffic monitoring in urban areas

2011 ◽  
Vol 59 (2) ◽  
pp. 137-140 ◽  
Author(s):  
S. Szczepański ◽  
M. Wöjcikowski ◽  
B. Pankiewicz ◽  
M. KŁosowski ◽  
R. Żaglewski
Keyword(s):  
Urban Areas ◽  
Traffic Monitoring ◽  
Detection Algorithm ◽  
Frame Rate ◽  
Integrated Sensor ◽  
The Road ◽  
Moving Vehicles ◽  
Direction Of Movement ◽  
Account Due ◽  
Selection Of

FPGA and ASIC implementation of the algorithm for traffic monitoring in urban areas This paper describes the idea and the implementation of the image detection algorithm, that can be used in integrated sensor networks for environment and traffic monitoring in urban areas. The algorithm is dedicated to the extraction of moving vehicles from real-time camera images for the evaluation of traffic parameters, such as the number of vehicles, their direction of movement and their approximate speed. The authors, apart from the careful selection of particular steps of the algorithm towards hardware implementation, also proposed novel improvements, resulting in increasing the robustness and the efficiency. A single, stationary, monochrome camera is used, simple shadow and highlight elimination is performed. The occlusions are not taken into account, due to placing the camera at a location high above the road. The algorithm is designed and implemented in pipelined hardware, therefore high frame-rate efficiency has been achieved. The algorithm has been implemented and tested in FPGA and ASIC.

Surface Image Edge Detection Algorithm Based on Grey Relational Analysis

2012 ◽  
Vol 461 ◽  
pp. 343-346 ◽  
Author(s):  
Gang Li ◽  
Ying Fang ◽  
Ya La Tong
Keyword(s):  
Edge Detection ◽  
Good Method ◽  
Detection Algorithm ◽  
Relational Model ◽  
Small Data ◽  
The Road ◽  
Image Edge Detection ◽  
Surface Image ◽  
Image Edge ◽  
Grey Relational

Automatic detection of pavement cracks is one of the very hot topics. For the characteristics of “small data, poor information” in the surface image processing, we construct ed a grey image relational model to characterize the local image edge feature, by selecting the appropriate threshold to extract the edge of appropriate level. Finally, simulation experiments show that the new algorithm can effectively improve the road edge detection results, and it is an effective good method worthy further study.

scholarly journals Comparable Encryption Scheme over Encrypted Cloud Data in Internet of Everything

2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Qian Meng ◽  
Jianfeng Ma ◽  
Kefei Chen ◽  
Yinbin Miao ◽  
Tengfei Yang
Keyword(s):  
Model Performance ◽  
Data Integrity ◽  
Cloud Service ◽  
Third Party ◽  
Encryption Scheme ◽  
Cloud Data ◽  
Practical Applications ◽  
Internet Of Everything ◽  
Window Method ◽  
Comparable Encryption

User authentication has been widely deployed to prevent unauthorized access in the new era of Internet of Everything (IOE). When user passes the legal authentication, he/she can do series of operations in database. We mainly concern issues of data security and comparable queries over ciphertexts in IOE. In traditional database, a Short Comparable Encryption (SCE) scheme has been widely used by authorized users to conduct comparable queries over ciphertexts, but existing SCE schemes still incur high storage and computational overhead as well as economic burden. In this paper, we first propose a basic Short Comparable Encryption scheme based on sliding window method (SCESW), which can significantly reduce computational and storage burden as well as enhance work efficiency. Unfortunately, as the cloud service provider is a semitrusted third party, public auditing mechanism needs to be furnished to protect data integrity. To further protect data integrity and reduce management overhead, we present an enhanced SCESW scheme based on position-aware Merkle tree, namely, PT-SCESW. Security analysis proves that PT-SCESW and SCESW schemes can guarantee completeness and weak indistinguishability in standard model. Performance evaluation indicates that PT-SCESW scheme is efficient and feasible in practical applications, especially for smarter and smaller computing devices in IOE.

Lane Detection Algorithm Based on Genetic Algorithm and its Parallel Computing Realization

2012 ◽  
Vol 479-481 ◽  
pp. 65-70
Author(s):  
Xiao Hui Zhang ◽  
Liu Qing ◽  
Mu Li
Keyword(s):  
Genetic Algorithm ◽  
Data Transmission ◽  
High Speed ◽  
High Performance ◽  
Large Data ◽  
Detection Algorithm ◽  
Lane Detection ◽  
The Road ◽  
Time Problem ◽  
High Speed Data

Based on the target detection of alignment template, the paper designs a lane alignment template by using correlation matching method, and combines with genetic algorithm for template stochastic matching and optimization to realize the lane detection. In order to solve the real-time problem of lane detection algorithm based on genetic algorithm, this paper uses the high performance multi-core DSP chip TMS320C6474 as the core, combines with high-speed data transmission technology of Rapid10, realizes the hardware parallel processing of the lane detection algorithm. By Rapid10 bus, the data transmission speed between the DSP and the DSP can reach 3.125Gbps, it basically realizes transmission without delay, and thereby solves the high speed transmission of the large data quantity between processor. The experimental results show that, no matter the calculated lane line, or the running time is better than the single DSP and PC at the parallel C6474 platform. In addition, the road detection is accurate and reliable, and it has good robustness.

scholarly journals APPLICATION OF MLS DATA TO THE ASSESSMENT OF SAFETY-RELATED FEATURES IN THE SURROUNDING AREA OF AUTOMATICALLY DETECTED PEDESTRIAN CROSSINGS

2018 ◽  
Vol XLII-2 ◽  
pp. 1067-1074 ◽  
Author(s):  
M. Soilán ◽  
B. Riveiro ◽  
A. Sánchez-Rodríguez ◽  
L. M. González-deSantos
Keyword(s):  
Laser Scanning ◽  
Geographical Information ◽  
Mobile Mapping ◽  
Cloud Data ◽  
Traffic Lights ◽  
The Road ◽  
Processing Module ◽  
Pedestrian Crossing ◽  
Mobile Mapping Systems ◽  
Mapping Systems

During the last few years, there has been a huge methodological development regarding the automatic processing of 3D point cloud data acquired by both terrestrial and aerial mobile mapping systems, motivated by the improvement of surveying technologies and hardware performance. This paper presents a methodology that, in a first place, extracts geometric and semantic information regarding the road markings within the surveyed area from Mobile Laser Scanning (MLS) data, and then employs it to isolate street areas where pedestrian crossings are found and, therefore, pedestrians are more likely to cross the road. Then, different safety-related features can be extracted in order to offer information about the adequacy of the pedestrian crossing regarding its safety, which can be displayed in a Geographical Information System (GIS) layer. These features are defined in four different processing modules: Accessibility analysis, traffic lights classification, traffic signs classification, and visibility analysis. The validation of the proposed methodology has been carried out in two different cities in the northwest of Spain, obtaining both quantitative and qualitative results for pedestrian crossing classification and for each processing module of the safety assessment on pedestrian crossing environments.

scholarly journals Rockfall Risk Analysis Based on Precise Topography using UAV

2020 ◽  
Vol 20 (4) ◽  
pp. 63-73
Author(s):  
Jaehee Choi ◽  
Namgyun Kim ◽  
Bongjin Choe ◽  
Byonghee Jun
Keyword(s):  
Point Cloud Data ◽  
University Campus ◽  
Digital Map ◽  
Cloud Data ◽  
The Road ◽  
Mountainous Regions ◽  
Digital Elevation ◽  
Surface Models ◽  
Aerial Vehicle ◽  
Elevation Model

In this study, the risk of rockfall on incision slopes adjacent to roads was evaluated using the RocFall program. The study area was a slope adjacent to the road leading to a university campus in Samcheok-si, Gangwon-do, with an area of 774 m<sup>2</sup> and an average slope of approximately 43°. A rock shed was installed at the lower zone of the slope. A 3D model of the terrain was generated based on point cloud data gathered using a UAV (unmanned aerial vehicle). Fast and accurate orthoimages were captured by UAV and high-resolution digital surface models (DSMs) were produced; these data were used to assess the risk of rockfall. Compared to terrain extraction using a digital elevation model (DEM) generated from an existing digital map, terrain extraction using a UAV was more effective in deriving results close to the actual situation in the field, especially for the analysis of rockfall jump height and kinetic energy. The necessity of constructing 3D topographic data using UAVs to predict rockfall disasters in mountainous regions was confirmed.

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