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| dc.contributor.author | Mohamed, Hussam El-Din | |
| dc.contributor.author | Fadl, Ali | |
| dc.contributor.author | Anas, Omar | |
| dc.contributor.author | Wageeh, Youssef | |
| dc.contributor.author | ElMasry, Noha | |
| dc.contributor.author | Nabil, Ayman | |
| dc.contributor.author | Atia, Ayman | |
| dc.date.accessioned | 2020-04-18T07:52:05Z | |
| dc.date.available | 2020-04-18T07:52:05Z | |
| dc.date.issued | 2020-02 | |
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| dc.identifier.other | https://doi.org/10.1016/j.procs.2020.03.123 | |
| dc.identifier.uri | https://t.ly/l513 | |
| dc.description | Scopus | |
| dc.description.abstract | Tasks involving the monitoring of fish farms such as controlling fish ponds is one of the expensive and difficult tasks for fish farmers. Usually, fish farmers are doing these tasks manually which costs them time and money. We propose a system that automates the monitoring of the fish farm. This paper presents a technique to enhance the detection of fish and their trajectories in challenging water conditions. Firstly, we used image enhancement techniques to enhance unclear water images and to better identify fish. Then, we applied an object detection algorithm to detect fish. Finally, the detected objects’ coordinates are then used to extract features like count and trajectories. All experiments were done on our experimental setup. The technique showed promising results in regards to detection and tracking accuracy when applied | en_US |
| dc.description.uri | https://www.scimagojr.com/journalsearch.php?q=19700182801&tip=sid&clean=0 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Elsevier | en_US |
| dc.relation.ispartofseries | Procedia Computer Science;170 (2020) 539–546 1877-0509 | |
| dc.subject | Fish farming | en_US |
| dc.subject | Object detection | en_US |
| dc.subject | Object detection | en_US |
| dc.subject | Image enhancement. | en_US |
| dc.title | MSR-YOLO: Method to Enhance Fish Detection and Tracking in Fish Farms | en_US |
| dc.type | Article | en_US |
| dc.identifier.doi | https://doi.org/10.1016/j.procs.2020.03.123 | |
| dc.Affiliation | October University for modern sciences and Arts (MSA) |
