Object Classification with Roadside LiDAR Data Using a Probabilistic Neural Network
Object classification is important information for different transportation areas. This research developed a probabilistic neural network (PNN) classifier for object classification using roadside Light Detection and Ranging (LiDAR). The objective was to classify the road user on the urban road into...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/7/803 |
| _version_ | 1797539784322711552 |
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| author | Jiancheng Zhang Rendong Pi Xiaohong Ma Jianqing Wu Hongtao Li Ziliang Yang |
| author_facet | Jiancheng Zhang Rendong Pi Xiaohong Ma Jianqing Wu Hongtao Li Ziliang Yang |
| author_sort | Jiancheng Zhang |
| collection | DOAJ |
| description | Object classification is important information for different transportation areas. This research developed a probabilistic neural network (PNN) classifier for object classification using roadside Light Detection and Ranging (LiDAR). The objective was to classify the road user on the urban road into one of four classes: Pedestrian, bicycle, passenger car, and truck. Five features calculated from the point cloud generated from the roadside LiDAR were selected to represent the difference between different classes. A total of 2736 records (2062 records for training, and 674 records for testing) were manually marked for training and testing the PNN algorithm. The data were collected at three different sites representing different scenarios. The performance of the classification was evaluated by comparing the result of the PNN with those of the support vector machine (SVM) and the random forest (RF). The comparison results showed that the PNN can provide the results of classification with the highest accuracy among the three investigated methods. The overall accuracy of the PNN for object classification was 97.6% using the testing database. The errors in the classification results were also diagnosed. Discussions about the direction of future studies were also provided at the end of this paper. |
| first_indexed | 2024-03-10T12:49:47Z |
| format | Article |
| id | doaj.art-e41d64ffb77349e69546e7eeb305becb |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T12:49:47Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-e41d64ffb77349e69546e7eeb305becb2023-11-21T13:09:50ZengMDPI AGElectronics2079-92922021-03-0110780310.3390/electronics10070803Object Classification with Roadside LiDAR Data Using a Probabilistic Neural NetworkJiancheng Zhang0Rendong Pi1Xiaohong Ma2Jianqing Wu3Hongtao Li4Ziliang Yang5School of Qilu Transportation, Shandong University, Jinan 250061, ChinaSchool of Qilu Transportation, Shandong University, Jinan 250061, ChinaShandong Zhengzhong Information Technology Co., Ltd., Jinan 250000, ChinaSchool of Qilu Transportation, Shandong University, Jinan 250061, ChinaShandong Zhengzhong Information Technology Co., Ltd., Jinan 250000, ChinaSchool of Qilu Transportation, Shandong University, Jinan 250061, ChinaObject classification is important information for different transportation areas. This research developed a probabilistic neural network (PNN) classifier for object classification using roadside Light Detection and Ranging (LiDAR). The objective was to classify the road user on the urban road into one of four classes: Pedestrian, bicycle, passenger car, and truck. Five features calculated from the point cloud generated from the roadside LiDAR were selected to represent the difference between different classes. A total of 2736 records (2062 records for training, and 674 records for testing) were manually marked for training and testing the PNN algorithm. The data were collected at three different sites representing different scenarios. The performance of the classification was evaluated by comparing the result of the PNN with those of the support vector machine (SVM) and the random forest (RF). The comparison results showed that the PNN can provide the results of classification with the highest accuracy among the three investigated methods. The overall accuracy of the PNN for object classification was 97.6% using the testing database. The errors in the classification results were also diagnosed. Discussions about the direction of future studies were also provided at the end of this paper.https://www.mdpi.com/2079-9292/10/7/803object classificationprobabilistic neural networkroadside LiDARpoint cloud |
| spellingShingle | Jiancheng Zhang Rendong Pi Xiaohong Ma Jianqing Wu Hongtao Li Ziliang Yang Object Classification with Roadside LiDAR Data Using a Probabilistic Neural Network Electronics object classification probabilistic neural network roadside LiDAR point cloud |
| title | Object Classification with Roadside LiDAR Data Using a Probabilistic Neural Network |
| title_full | Object Classification with Roadside LiDAR Data Using a Probabilistic Neural Network |
| title_fullStr | Object Classification with Roadside LiDAR Data Using a Probabilistic Neural Network |
| title_full_unstemmed | Object Classification with Roadside LiDAR Data Using a Probabilistic Neural Network |
| title_short | Object Classification with Roadside LiDAR Data Using a Probabilistic Neural Network |
| title_sort | object classification with roadside lidar data using a probabilistic neural network |
| topic | object classification probabilistic neural network roadside LiDAR point cloud |
| url | https://www.mdpi.com/2079-9292/10/7/803 |
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