To enhance environmental perception in autonomous driving and address the shortcomings of single-task methods, this study introduces a multi-task perception network called You Only Look Once for Panoptic Driving Perce...
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Renewable energy sources, such as hydropower, solar power, and wind power, have the capacity to efficiently supply their respective portions of the world’s energy needs. Since then, the use of renewable energy in ele...
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Biometric recognition is used across a variety of applications from cyber security to border security. Recent research has focused on ensuring biometric performance (false negatives and false positives) is fair across...
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This paper presents a study for subsurface object detection using different scanning acquisition paths. The air coupled system is considered, which can be easily attached to the unnamed aerial vehicle (UAV). The objec...
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Circuit quantum electrodynamics (cQED) is one of the most promising platforms for building quantum information processors. As quantum technology evolves the hardware is becoming increasingly complex, so there is a nee...
The growing problem of water scarcity is made worse by the unchecked use of fossil fuels for irrigation water-table pumping, which contributes to both environmental degradation and global warming. An burgeoning popula...
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Ground plane detection and segmentation techniques can benefit and help improve the accuracy and robustness of a wide range of computer vision applications, from 3D object segmentation and autonomous navigation to mix...
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ISBN:
(纸本)9798350382204
Ground plane detection and segmentation techniques can benefit and help improve the accuracy and robustness of a wide range of computer vision applications, from 3D object segmentation and autonomous navigation to mixed and augmented reality. Existing approaches often rely on restrictive assumptions to simplify the problem, such as the ground plane being the largest plane in the scene or the camera location or orientation being ideal. We present a ground plane segmentation technique for real-world 3D indoor scenes where the position and orientation of the sensor are unrestricted and unknown. Our method only requires one 3D point cloud of an indoor scene and assumes that the scene contains at least one surface parallel to the actual ground plane, which is generally true for 3D indoor scenes. We begin by utilizing a voxelized grid downsampling method to enhance the speed of the algorithm. Subsequently, we use K-medoids clustering and an angular-based zone determination technique to identify the ground zone. Next, we divide the ground zone into several clusters using the Euclidean clustering algorithm, and we employ the M-estimator Sample Consensus (MSAC) algorithm to fit the largest plane in each cluster with a specific orientation. Finally, based on the geometric relationship between the fitted planes of the ground zone, we estimate the ground plane, verify it and segment all its associated points using a distance-based approach. We evaluated our method on public and self-generated datasets, in which we positioned a depth sensor at various locations, pitches, and yaws. Our experimental results demonstrate that our proposed method can robustly and efficiently detect and segment the ground plane of complex 3D indoor scenes and supports varied sensor locations and orientations. We evaluate the performance of our proposed method in terms of four conventional metrics: specificity, precision, recall, and F1 score, with average experimental results of 98.28, 95.48, 96.64, a
This paper explores the use of reinforcement learning and various machine learning techniques to optimize the configurations of Hyperledger Fabric v2 Channels and Orderers. Our goal is to increase the average throughp...
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Unmanned aerial vehicles (UAVs) equipped with various sensors and onboard processing capabilities have emerged as a promising means to acquire field data for precision agriculture applications. However, such UAVs are ...
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