ETLi: Efficiently annotated traffic LiDAR dataset using incremental and suggestive annotation

Kang, Jun-Gyu; Han, Seung‐Jun; Kim, Nahyeon; Min, Kyoung Wook

doi:10.4218/etrij.2021-0055

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…As mentioned previously, the proposed method was developed to improve the performance of semantic segmentation models for autonomous driving. Semantic segmentation uses suitable benchmark datasets consisting of data collected from vehicles [16][17][18]45,46].…”

Section: Methodsmentioning

confidence: 99%

CRFNet: Context ReFinement Network used for semantic segmentation

An,

Kang,

Choi

et al. 2023

ETRI Journal

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Recent semantic segmentation frameworks usually combine low‐level and high‐level context information to achieve improved performance. In addition, postlevel context information is also considered. In this study, we present a Context ReFinement Network (CRFNet) and its training method to improve the semantic predictions of segmentation models of the encoder–decoder structure. Our study is based on postprocessing, which directly considers the relationship between spatially neighboring pixels of a label map, such as Markov and conditional random fields. CRFNet comprises two modules: a refiner and a combiner that, respectively, refine the context information from the output features of the conventional semantic segmentation network model and combine the refined features with the intermediate features from the decoding process of the segmentation model to produce the final output. To train CRFNet to refine the semantic predictions more accurately, we proposed a sequential training scheme. Using various backbone networks (ENet, ERFNet, and HyperSeg), we extensively evaluated our model on three large‐scale, real‐world datasets to demonstrate the effectiveness of our approach.

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Section: Methodsmentioning

confidence: 99%

CRFNet: Context ReFinement Network used for semantic segmentation

An,

Kang,

Choi

et al. 2023

ETRI Journal

Self Cite

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“…Recent breakthroughs in convolutional neural networks (CNNs) and the release of large datasets, including 3D LiDAR datasets, have rapidly improved 3D object detection [15,16]. CNNs for 3D point-cloud encoding extract local features from adjacent points grouped by a distance metric [5] or voxel features within equally spaced 3D voxels [17].…”

Section: Object Detection Using Only 3d Lidarmentioning

confidence: 99%

Real‐time 3D multi‐pedestrian detection and tracking using 3D LiDAR point cloud for mobile robot

Na,

Park

2023

ETRI Journal

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Mobile robots are used in modern life; however, object recognition is still insufficient to realize robot navigation in crowded environments. Mobile robots must rapidly and accurately recognize the movements and shapes of pedestrians to navigate safely in pedestrian‐rich spaces. This study proposes real‐time, accurate, three‐dimensional (3D) multi‐pedestrian detection and tracking using a 3D light detection and ranging (LiDAR) point cloud in crowded environments. The pedestrian detection quickly segments a sparse 3D point cloud into individual pedestrians using a lightweight convolutional autoencoder and connected‐component algorithm. The multi‐pedestrian tracking identifies the same pedestrians considering motion and appearance cues in continuing frames. In addition, it estimates pedestrians' dynamic movements with various patterns by adaptively mixing heterogeneous motion models. We evaluate the computational speed and accuracy of each module using the KITTI dataset. We demonstrate that our integrated system, which rapidly and accurately recognizes pedestrian movement and appearance using a sparse 3D LiDAR, is applicable for robot navigation in crowded spaces.

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“…These techniques provide vital enlightenment and boosting in autonomous driving. Considering there are sequentially arriving multi-modal data acquired by multi-modal sensors, the joint interpretation for multi-modal incremental data is an urgent task which has been explored from 3D semantic mapping [178], [179], multi-view cooperative interpretation [180], [181], [182], LiDAR data interpretation [183], federated learning [184], domain generalization [185], and visual-language collaboration [186], etc. In the field of remote sensing, research focuses on enhancing small objects [187], multi-level distillation [44], [151], [188] and multisource [20] unsupervised domain-incremental CSS.…”

Section: Other Routesmentioning

confidence: 99%

Ecosystem Health of the Beiyun River Basin (Beijing, China) as Evaluated by the Analytic Hierarchy Process Method

Yuan

Zhao

et al. 2021

Preprint

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Ecosystem services provided by river ecosystems rely on healthy ecosystem structure and ecological processes. The Beijing-Tianjin-Hebei urban is a typical water-deficient area. As an important part of the urban-rural integration construction, evaluating the health status of the Beiyun River Basin and discovering the weak links in the water environment is the basis for improving the health of the basin. In this study, Analytic Hierarchy Process was used to establish an evaluation index system for the Beiyun River Basin from 5 aspects including water quality, biology, habitat, hydrology, and social functions, and to assign weights to the index layer. The evaluation results showed that the health evaluation results of the Beiyun River Basin in 2019 are “sub-healthy”, and the overall health status is gradually worsening from northwest to southeast. In the middle reaches of the region, the evaluation result is "healthy", followed by the upstream, and the downstream is the worst. The results showed that areas with less human interference are in better health. The factors that affect the overall health evaluation status in the basin are the level of nutrition, biodiversity, and vegetation coverage. For the comprehensive management of the Beiyun River, the improvement of water quality and habitat ecological restoration is the key to the health of the upstream ecosystem health status. The improvement of the health status of the downstream should focus on equal emphasis on water quality and quantity, restoration of biodiversity, and improvement of the quality of the riparian ecological environment.

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ETLi: Efficiently annotated traffic LiDAR dataset using incremental and suggestive annotation

Cited by 5 publications

References 29 publications

CRFNet: Context ReFinement Network used for semantic segmentation

CRFNet: Context ReFinement Network used for semantic segmentation

Real‐time 3D multi‐pedestrian detection and tracking using 3D LiDAR point cloud for mobile robot

Ecosystem Health of the Beiyun River Basin (Beijing, China) as Evaluated by the Analytic Hierarchy Process Method

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