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Roadside LiDAR has become an important sensor for the detection of objects in cities, such as vehicles and pedestrians, which is due to its advantages of all-weather operation and high-ranging accuracy. In order to serve an intelligent transportation system, the efficient and accurate segmentation of vehicles and pedestrians is needed in the covera...
Contexts in source publication
Context 1
... (b) By referring to the LiDAR manual, the vertical angle of the detector module that corresponds to each laser ID can be obtained, as is shown in Table 1. ...Context 2
... roads are generally two-way lanes with heavy traffic and with many vehicles and pedestrians. In addition, the environment at the intersection of urban roads is By referring to the LiDAR manual, the vertical angle of the detector module that corresponds to each laser ID can be obtained, as is shown in Table 1. Urban roads are generally two-way lanes with heavy traffic and with many vehicles and pedestrians. ...Context 3
... polar grid has two parameters: the number of vertical columns (θ) and the number of horizontal rows (ϕ). For the selected LiDAR, according to Table 1, each laser ID corresponds to a column (there is a total of 32 columns), and the angle of each column is the vertical angle, and so the number of vertical columns (θ) can be determined. The number of horizontal rows (ϕ) of the polar grid is determined by Formula (4). ...Context 4
... polar grid has two parameters: the number of vertical columns (í µí¼) and the number of horizontal rows (í µí¼). For the selected LiDAR, according to Table 1, each laser ID corresponds to a column (there is a total of 32 columns), and the angle of each column is the vertical angle, and so the number of vertical columns (í µí¼) can be determined. The number of horizontal rows (í µí¼) of the polar grid is determined by Formula (4). ...Similar publications
Vehicular accident prediction and detection has recently garnered curiosity and large amounts of attention in machine learning applications and related areas, due to its peculiar and fascinating application potentials in the development of Intelligent Transportation Systems (ITS) that play a pivotal role in the success of emerging smart cities. In...
Citations
... Its purpose is the classification and segmentation of point cloud data by learning local geometric features and representation compositions. An algorithm based on the adaptive polar grid Gaussian mixture model was introduced for the segmentation of roadside LiDAR data [13]. This Agronomy 2023, 13, 2806 3 of 27 method is designed to segment both the foreground and background and to cluster vehicles and pedestrians, thereby reducing computational complexity and processing time. ...
The segmentation of individual pests is a prerequisite for pest feature extraction and identification. To address the issue of pest adhesion in the apple orchard pest identification process, this research proposed a pest adhesion image segmentation method based on Gaussian Mixture Model with Density and Curvature Weighting (GMM-DC). First, in the HSV color space, an image was desaturated by adjusting the hue and inverting to mitigate threshold crossing points. Subsequently, threshold segmentation and contour selection methods were used to separate the image background. Next, a shape factor was introduced to determine the regions and quantities of adhering pests, thereby determining the number of model clustering clusters. Then, point cloud reconstruction was performed based on the color and spatial distribution features of the pests. To construct the GMM-DC segmentation model, a spatial density (SD) and spatial curvature (SC) information function were designed and embedded in the GMM. Finally, experimental analysis was conducted on the collected apple orchard pest images. The results showed that GMM-DC achieved an average accurate segmentation rate of 95.75%, an average over-segmentation rate of 2.83%, and an average under-segmentation rate of 1.42%. These results significantly outperformed traditional image segmentation methods. In addition, the original and improved Mask R-CNN models were used as recognition models, and the mean Average Precision was used as the evaluation metric. Recognition experiments were conducted on pest images with and without the proposed method. The results show the mean Average Precision for pest images segmented with the proposed method as 92.43% and 96.75%. This indicates an improvement of 13.01% and 12.18% in average recognition accuracy, respectively. The experimental results demonstrate that this method provides a theoretical and methodological foundation for accurate pest identification in orchards.
... Lidar, especially the time-of-flight (TOF) variant, is widely used in autonomous vehicles to acquire point cloud data, aiding in object detection, localization, and map construction. Integrating lidar into roadside infrastructure is emerging, and some studies have been conducted using roadside lidar [1][2][3][4][5][6][7][8][9][10]. ...
Sensors on autonomous vehicles have inherent physical constraints. To address these limitations, several studies have been conducted to enhance sensing capabilities by establishing wireless communication between infrastructure and autonomous vehicles. Various sensors are strategically positioned within the road infrastructure, providing essential sensory data to these vehicles. The primary challenge lies in sensor placement, as it necessitates identifying optimal locations that minimize blind spots while maximizing the sensor’s coverage area. Therefore, to solve this problem, a method for positioning multiple sensor systems in road infrastructure is proposed. By introducing a voxel grid, the problem is formulated as an optimization challenge, and a genetic algorithm is employed to find a solution. Experimental findings using lidar sensors are presented to demonstrate the efficacy of this proposed approach.
... However, in the case of traffic congestion, it is difficult to choose the grid with the appropriate size to create the background model. Therefore, an adaptive polar grid Gaussian mixture model (APG-GMM) was proposed, which divides the threedimensional grid based on the vertical angle and horizontal angle resolution of the roadside LiDAR, and a Gaussian mixture background model with the maximum distance value as the hyperparameter for the point cloud in each three-dimensional grid was established [52]. This method improves the accuracy of foreground and background segmentation. ...
Light Detection and Ranging (LiDAR) technology has the advantages of high detection accuracy, a wide range of perception, and not being affected by light. The 3D LiDAR is placed at the commanding height of the traffic scene, the overall situation can be grasped from the perspective of top view, and the trajectory of each object in the traffic scene can be accurately perceived in real time, and then the object information can be distributed to the surrounding vehicles or other roadside LiDAR through advanced wireless communication equipment, which can significantly improve the local perception ability of an autonomous vehicle. This paper first describes the characteristics of roadside LiDAR and the challenges of object detection and then reviews in detail the current methods of object detection based on a single roadside LiDAR and multi-LiDAR cooperatives. Then, some studies for roadside LiDAR perception in adverse weather and datasets released in recent years are introduced. Finally, some current open challenges and future works for roadside LiDAR perception are discussed. To the best of our knowledge, this is the first work to systematically study roadside LiDAR perception methods and datasets. It has an important guiding role in further promoting the research of roadside LiDAR perception for practical applications.
