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http://asiair.asia.edu.tw/ir/handle/310904400/115596
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| Title: | The NITRDrone Dataset to Address the Challenges for Road Extraction from Aerial Images |
| Authors: | Kumar, Tanmay;Behera, Tanmay Kumar;Baksh, Sambit;Bakshi, Sambit;Kumar, Pankaj;Sa, Pankaj Kumar;Napp, Michele;Nappi, Michele;Cast, Aniello;Castiglione, Aniello;Vijaya, Pandi;Vijayakumar, Pandi;Bhoosha, Brij;Gupta, Brij Bhooshan |
| Contributors: | 資訊電機學院資訊工程學系 |
| Keywords: | AI· aerial image· Semantic segmentation· CNN· UAV |
| Date: | 2022-07-01 |
| Issue Date: | 2023-03-29 02:50:30 (UTC+0) |
| Publisher: | 亞洲大學 |
| Abstract: | Recent years have witnessed a dramatic evolution in small-scale remote sensors such as Unmanned aerial vehicles (UAVs). Characteristics such as automatic flight control, flight time, and image acquisition have fueled various computer-vision tasks, providing better efficiency and usefulness than fixed viewing surveillance cameras. However, in constrained scenarios, the number of UAV-based aerial datasets is still low, which comparatively focuses on specific tasks such as image segmentation. In this paper, we present a high-resolution UAV-based image-dataset, named “NITRDrone” focusing on aerial image segmentation tasks especially extracting the road networks from the aerial images. The images and video sequences in this dataset are captured over different locations of the NITR campus area, covering around 650 acres. Thus, it provides many diversified scenarios to be considered while analyzing aerial images. In particular, the dataset is prepared to address the existing challenges in UAV-based aerial image segmentation problems. Extensive experiments have been conducted to prove the effectiveness of the proposed dataset to address the aerial segmentation problems through the existing state-of-the-art methodologies. Out of the considered baseline methodologies, U-Net performs the best with an intersection of union (IoU) of 0.77, followed DeepLabplusException (IoU: 0.74) and SegNet (IoU: 0.68). We hope the NITRDrone dataset will encourage the researchers while boosting the research and development in the visual analysis of UAV platforms. The NITRDrone dataset is available online at: [https://github.com/drone-vision/NITRDrone-Dataset]. |
| Appears in Collections: | [資訊工程學系] 期刊論文
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