High-Resolution Representation for Mobile Mapping Data in Curved Regular Grid Model

Su, Jingxin; Miyazaki, Ryuji; Tamaki, Toru; Kaneda, Kazufumi

doi:10.3390/s19245373

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…There are three common methods of image size standardization: nearest neighbor method, bilinear interpolation method and cubic interpolation method. Considering the factors of accuracy and operation speed, this paper adopts bilinear interpolation method [24] to standardize the image size.…”

Section: E Image Size Standardizationmentioning

confidence: 99%

Fish Shoals Behavior Detection Based on Convolutional Neural Network and Spatiotemporal Information

et al. 2020

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Behavior is the first visible change in an animal species after exposure to its own or environmental stressors and is a sensitive indicator. Fish are social animals, and the abnormality of group behavior is more indicative about a particular event than individual behavior, providing more effective informeqation about environmental or group social changes. The group behavior is not only reflected in the spatial distribution, but also reflected in the temporal behavior of the group and individual movement changes under the influence of pressure factors. This paper proposes a group behavior discrimination method based on convolutional neural network and spatiotemporal information fusion, which intends to make use of the prominent performance of convolutional neural network in image recognition and state classification, and imitating the attentional mechanism of ventral channel and dorsal channel when the human brain processes visual signals. Some pressure environments are made in laboratory, the behavior states of fish shoals are recorded, and the sample database of shoals' behavior state is established by combining the spatial information of shoals' spatial distribution with the time information reflected in the movement behavior. A simple convolutional neural network is constructed to quickly identify the behavior state of fish shoals. The effects of bath size and training epoch on network training speed and recognition accuracy are discussed, and the visualization of the intermediate data of the convolutional neural network is studied. Shown from the results of experiments of this paper, different behavior states of fish shoals can be recognized and classified effectively by using the simple convolutional neural network and spatiotemporal fusion images. What's more, from the visualization of network intermediate data, it is found that the convolutional neural network has a higher discrimination power to the image edge feature than the image gray-value feature.INDEX TERMS Intelligent agriculture, fish behavior, deep learning, convolutional neural network, spatiotemporal information fusion.

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Section: E Image Size Standardizationmentioning

confidence: 99%

Fish Shoals Behavior Detection Based on Convolutional Neural Network and Spatiotemporal Information

et al. 2020

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“…The modeling of road scenes has become an increasingly important topic in academia and industry [1]. This importance comes from various applications that rely on accurate and detailed representations of road environments, such as vehicle dynamics simulation, pavement assessment, reconstruction design, and transportation planning [2].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Road Surface Segmentation in Non-Urban Environments: A 3D Point Cloud Approach With Grid Structure and Shallow Neural Networks

Dowajy,

Somogyi,

Barsi

et al. 2024

IEEE Access

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Automatic road segmentation from three-dimensional point cloud data has gained increasing interest recently. However, it is still challenging to do this task automatically due to the wide variations of roads and complex environments, especially in non-urban areas. This research proposed a comprehensive approach for using shallow neural networks to segment non-urban road point clouds to support autonomous driving applications. The proposed approach involves converting raw point cloud data into a regular grid of cells or partial clouds. Initially, a shallow neural network based on cells' properties (cell plane fitting error, cell average intensity, cell elevation range, and cell weighted density) was employed to extract road cells from raw point cloud data. The road cells were refined and segmented into inside-road and road border point clouds based on morphologic operations. A point-wise shallow neural network was used to extract road points from the border point clouds based on intensity and geometric features (roughness, curvature, and change rate of the normal). A precise road surface point cloud is obtained by merging the inside-road and filtered border point clouds. The method performance was evaluated for two datasets captured using a mobile laser scanner (MLS). In the first dataset, the road points were extracted at average completeness, correctness, quality, and overall accuracy of 98.40%, 99.13%, 97.56%, and 98.47%, respectively. Similarly, the method achieved high scores for the second dataset with 97.22% completeness, 99.02% correctness, 96.29% quality, and 98.71% overall accuracy. The method performance demonstrates an advancement when compared to various state-of-the-art methods and also confirms its adaptability to different road environments.INDEX TERMS road segmentation, 3D point cloud, shallow neural network, cell point cloud, plan fitting, weighted density, geometric features.

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“…Barsi et al (2020) also produced OpenCRG model for straight road segment from TLS scans. Su et al (2019) derived OpenCRG models from highway network MMS data.…”

Section: Introduction Introduction Introduction Introductionmentioning

confidence: 99%

Comparative analysis of Road Scanning Techniques

et al. 2023

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A three-dimensional road point cloud is not only useful for civil engineers (road rehabilitation, road condition assessment) but can also be useful for vehicle engineers (autonomous vehicle driving scenario, vehicle dynamics simulation). Currently, there are several scanning techniques can be used to obtain these point clouds, such as terrestrial laser scanning (TLS), mobile laser scanning (MLS), airborne laser scanning (ALS), unmanned aerial vehicle (UAV) photogrammetry or UAV laser scanning. This paper discusses the investigation of four road surface scanning techniques by comparing their point clouds and the derived products. The comparison was performed for a section of a road with 1136 m length and 4 m width, the TLS survey provided the reference data. Aspects of point cloud evaluation included geometric accuracy, density, and the parameters of plane-fitting. CRG models were created from all studied point clouds to compare the difference between the final products to be used by the automotive industry. The results show that the MLS and the UAV photogrammetry generated the most accurate point cloud, while UAV laser scanning accuracy was the lowest. Similarly, the CRG models comparison showed that there was no significant difference between MLS and TLS models, and the UAV photogrammetry gave a smoother variation relative to the reference surface. Whereas the largest differences were noted for the CRG model derived from the UAV laser scanning models.

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High-Resolution Representation for Mobile Mapping Data in Curved Regular Grid Model

Cited by 6 publications

References 26 publications

Fish Shoals Behavior Detection Based on Convolutional Neural Network and Spatiotemporal Information

Fish Shoals Behavior Detection Based on Convolutional Neural Network and Spatiotemporal Information

An Automatic Road Surface Segmentation in Non-Urban Environments: A 3D Point Cloud Approach With Grid Structure and Shallow Neural Networks

Comparative analysis of Road Scanning Techniques

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