iVS Dataset and ezLabel: A Dataset and a Data Annotation Tool for Deep Learning Based ADAS Applications
To overcome the limitations of standard datasets with data at a wide-variety of scales and captured in the various conditions necessary to train neural networks to yield efficient results in ADAS applications, this paper presents a self-built open-to-free-use ‘iVS dataset’ and a data annotation tool...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/4/833 |
| _version_ | 1797476814916943872 |
|---|---|
| author | Yu-Shu Ni Vinay M. Shivanna Jiun-In Guo |
| author_facet | Yu-Shu Ni Vinay M. Shivanna Jiun-In Guo |
| author_sort | Yu-Shu Ni |
| collection | DOAJ |
| description | To overcome the limitations of standard datasets with data at a wide-variety of scales and captured in the various conditions necessary to train neural networks to yield efficient results in ADAS applications, this paper presents a self-built open-to-free-use ‘iVS dataset’ and a data annotation tool entitled ‘ezLabel’. The iVS dataset is comprised of various objects at different scales as seen in and around real driving environments. The data in the iVS dataset are collected by employing a camcorder in vehicles driving under different conditions, e.g., light, weather and traffic, and driving scenarios ranging from city traffic during peak and normal hours to freeway traffics during busy and normal conditions. Thus, the collected data are wide-ranging and captured all possible objects at various scales appearing in real-time driving situations. The data collected in order to build the dataset has to be annotated before use in training the CNNs and so this paper presents an open-to-free-use data annotation tool, ezLabel, for data annotation purposes as well. |
| first_indexed | 2024-03-09T21:09:05Z |
| format | Article |
| id | doaj.art-4aa0332306a347a9a663bbd86966f087 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:09:05Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4aa0332306a347a9a663bbd86966f0872023-11-23T21:52:57ZengMDPI AGRemote Sensing2072-42922022-02-0114483310.3390/rs14040833iVS Dataset and ezLabel: A Dataset and a Data Annotation Tool for Deep Learning Based ADAS ApplicationsYu-Shu Ni0Vinay M. Shivanna1Jiun-In Guo2Department of Electrical Engineering, Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu 30010, TaiwanDepartment of Electrical Engineering, Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu 30010, TaiwanDepartment of Electrical Engineering, Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu 30010, TaiwanTo overcome the limitations of standard datasets with data at a wide-variety of scales and captured in the various conditions necessary to train neural networks to yield efficient results in ADAS applications, this paper presents a self-built open-to-free-use ‘iVS dataset’ and a data annotation tool entitled ‘ezLabel’. The iVS dataset is comprised of various objects at different scales as seen in and around real driving environments. The data in the iVS dataset are collected by employing a camcorder in vehicles driving under different conditions, e.g., light, weather and traffic, and driving scenarios ranging from city traffic during peak and normal hours to freeway traffics during busy and normal conditions. Thus, the collected data are wide-ranging and captured all possible objects at various scales appearing in real-time driving situations. The data collected in order to build the dataset has to be annotated before use in training the CNNs and so this paper presents an open-to-free-use data annotation tool, ezLabel, for data annotation purposes as well.https://www.mdpi.com/2072-4292/14/4/833ADASdatadatasetannotationobject detectionscales and sizes |
| spellingShingle | Yu-Shu Ni Vinay M. Shivanna Jiun-In Guo iVS Dataset and ezLabel: A Dataset and a Data Annotation Tool for Deep Learning Based ADAS Applications Remote Sensing ADAS data dataset annotation object detection scales and sizes |
| title | iVS Dataset and ezLabel: A Dataset and a Data Annotation Tool for Deep Learning Based ADAS Applications |
| title_full | iVS Dataset and ezLabel: A Dataset and a Data Annotation Tool for Deep Learning Based ADAS Applications |
| title_fullStr | iVS Dataset and ezLabel: A Dataset and a Data Annotation Tool for Deep Learning Based ADAS Applications |
| title_full_unstemmed | iVS Dataset and ezLabel: A Dataset and a Data Annotation Tool for Deep Learning Based ADAS Applications |
| title_short | iVS Dataset and ezLabel: A Dataset and a Data Annotation Tool for Deep Learning Based ADAS Applications |
| title_sort | ivs dataset and ezlabel a dataset and a data annotation tool for deep learning based adas applications |
| topic | ADAS data dataset annotation object detection scales and sizes |
| url | https://www.mdpi.com/2072-4292/14/4/833 |
| work_keys_str_mv | AT yushuni ivsdatasetandezlabeladatasetandadataannotationtoolfordeeplearningbasedadasapplications AT vinaymshivanna ivsdatasetandezlabeladatasetandadataannotationtoolfordeeplearningbasedadasapplications AT jiuninguo ivsdatasetandezlabeladatasetandadataannotationtoolfordeeplearningbasedadasapplications |