Development of a Large-Scale Roadside Facility Detection Model Based on the Mapillary Dataset
The detection of road facilities or roadside structures is essential for high-definition (HD) maps and intelligent transportation systems (ITSs). With the rapid development of deep-learning algorithms in recent years, deep-learning-based object detection techniques have provided more accurate and ef...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/24/9992 |
| _version_ | 1797455347422593024 |
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| author | Zhehui Yang Chenbo Zhao Hiroya Maeda Yoshihide Sekimoto |
| author_facet | Zhehui Yang Chenbo Zhao Hiroya Maeda Yoshihide Sekimoto |
| author_sort | Zhehui Yang |
| collection | DOAJ |
| description | The detection of road facilities or roadside structures is essential for high-definition (HD) maps and intelligent transportation systems (ITSs). With the rapid development of deep-learning algorithms in recent years, deep-learning-based object detection techniques have provided more accurate and efficient performance, and have become an essential tool for HD map reconstruction and advanced driver-assistance systems (ADASs). Therefore, the performance evaluation and comparison of the latest deep-learning algorithms in this field is indispensable. However, most existing works in this area limit their focus to the detection of individual targets, such as vehicles or pedestrians and traffic signs, from driving view images. In this study, we present a systematic comparison of three recent algorithms for large-scale multi-class road facility detection, namely Mask R-CNN, YOLOx, and YOLOv7, on the Mapillary dataset. The experimental results are evaluated according to the recall, precision, mean F1-score and computational consumption. YOLOv7 outperforms the other two networks in road facility detection, with a precision and recall of 87.57% and 72.60%, respectively. Furthermore, we test the model performance on our custom dataset obtained from the Japanese road environment. The results demonstrate that models trained on the Mapillary dataset exhibit sufficient generalization ability. The comparison presented in this study aids in understanding the strengths and limitations of the latest networks in multiclass object detection on large-scale street-level datasets. |
| first_indexed | 2024-03-09T15:52:07Z |
| format | Article |
| id | doaj.art-f9e09cf1aa8f49beb5ac3c0e21dc5d55 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T15:52:07Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f9e09cf1aa8f49beb5ac3c0e21dc5d552023-11-24T17:58:44ZengMDPI AGSensors1424-82202022-12-012224999210.3390/s22249992Development of a Large-Scale Roadside Facility Detection Model Based on the Mapillary DatasetZhehui Yang0Chenbo Zhao1Hiroya Maeda2Yoshihide Sekimoto3Center for Spatial Information Science, The University of Tokyo, Tokyo 277-8568, JapanCenter for Spatial Information Science, The University of Tokyo, Tokyo 277-8568, JapanUrban X Technologies, Shibuya-ku, Tokyo 150-0002, JapanCenter for Spatial Information Science, The University of Tokyo, Tokyo 277-8568, JapanThe detection of road facilities or roadside structures is essential for high-definition (HD) maps and intelligent transportation systems (ITSs). With the rapid development of deep-learning algorithms in recent years, deep-learning-based object detection techniques have provided more accurate and efficient performance, and have become an essential tool for HD map reconstruction and advanced driver-assistance systems (ADASs). Therefore, the performance evaluation and comparison of the latest deep-learning algorithms in this field is indispensable. However, most existing works in this area limit their focus to the detection of individual targets, such as vehicles or pedestrians and traffic signs, from driving view images. In this study, we present a systematic comparison of three recent algorithms for large-scale multi-class road facility detection, namely Mask R-CNN, YOLOx, and YOLOv7, on the Mapillary dataset. The experimental results are evaluated according to the recall, precision, mean F1-score and computational consumption. YOLOv7 outperforms the other two networks in road facility detection, with a precision and recall of 87.57% and 72.60%, respectively. Furthermore, we test the model performance on our custom dataset obtained from the Japanese road environment. The results demonstrate that models trained on the Mapillary dataset exhibit sufficient generalization ability. The comparison presented in this study aids in understanding the strengths and limitations of the latest networks in multiclass object detection on large-scale street-level datasets.https://www.mdpi.com/1424-8220/22/24/9992object detectionYOLOv7YOLOxMask R-CNNITSHD map |
| spellingShingle | Zhehui Yang Chenbo Zhao Hiroya Maeda Yoshihide Sekimoto Development of a Large-Scale Roadside Facility Detection Model Based on the Mapillary Dataset Sensors object detection YOLOv7 YOLOx Mask R-CNN ITS HD map |
| title | Development of a Large-Scale Roadside Facility Detection Model Based on the Mapillary Dataset |
| title_full | Development of a Large-Scale Roadside Facility Detection Model Based on the Mapillary Dataset |
| title_fullStr | Development of a Large-Scale Roadside Facility Detection Model Based on the Mapillary Dataset |
| title_full_unstemmed | Development of a Large-Scale Roadside Facility Detection Model Based on the Mapillary Dataset |
| title_short | Development of a Large-Scale Roadside Facility Detection Model Based on the Mapillary Dataset |
| title_sort | development of a large scale roadside facility detection model based on the mapillary dataset |
| topic | object detection YOLOv7 YOLOx Mask R-CNN ITS HD map |
| url | https://www.mdpi.com/1424-8220/22/24/9992 |
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