Show simple item record

dc.contributor.author Wang, CC
dc.contributor.author Samani, H
dc.contributor.author Yang, CY
dc.contributor.editor Sudantha, BH
dc.date.accessioned 2022-11-18T08:04:14Z
dc.date.available 2022-11-18T08:04:14Z
dc.date.issued 2019-12
dc.identifier.citation C. -C. Wang, H. Samani and C. -Y. Yang, "Object Detection with Deep Learning for Underwater Environment," 2019 4th International Conference on Information Technology Research (ICITR), 2019, pp. 1-6, doi: 10.1109/ICITR49409.2019.9407797. en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/19568
dc.description.abstract In this research we have investigated the usage of deep learning algorithms for object detection in underwater environment and specifically we have employed YOLOv3 algorithm in our study. Details of the algorithm and experimental results are presented. We used available underwater database for training and investigated the method by experimenting to detect and identify the type of the fish in an aquarium in the lab. The results are also explained in this paper. en_US
dc.language.iso en en_US
dc.publisher Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa, Sri Lanka en_US
dc.relation.uri https://ieeexplore.ieee.org/document/9407797 en_US
dc.subject Deep Learning en_US
dc.subject Object Detection en_US
dc.subject YOLO en_US
dc.subject Underwater en_US
dc.title Object detection with deep learning for underwater environment en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty IT en_US
dc.identifier.department Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. en_US
dc.identifier.year 2019 en_US
dc.identifier.conference 4th International Conference in Information Technology Research 2019 en_US
dc.identifier.place Colombo,Sri Lanka en_US
dc.identifier.proceeding Proceedings of the 4th International Conference in Information Technology Research 2019 en_US
dc.identifier.doi doi: 10.1109/ICITR49409.2019.9407797 en_US


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

  • ICITR - 2019 [19]
    International Conference on Information Technology Research (ICITR)

Show simple item record