Object detection in inland vessels using combined trained and pretrained models of YOLO8
<p><span class="fontstyle0">Abstract</span><span class="fontstyle1">—One of the main challenges in computer vision is<br />object detection, which entails both locating and identifying<br />specific items on an image. With a fresh perspective,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Academy Publishing Center
2023-11-01
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| Series: | Advances in Computing and Engineering |
| Online Access: | http://apc.aast.edu/ojs/index.php/ACE/article/view/669 |
| _version_ | 1797258890292756480 |
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| author | Ahmad A. Goudah Maximilian Jarofka Mohmed El-Habrouk Dieter Schramm Yasser G. Dessouky |
| author_facet | Ahmad A. Goudah Maximilian Jarofka Mohmed El-Habrouk Dieter Schramm Yasser G. Dessouky |
| author_sort | Ahmad A. Goudah |
| collection | DOAJ |
| description | <p><span class="fontstyle0">Abstract</span><span class="fontstyle1">—One of the main challenges in computer vision is<br />object detection, which entails both locating and identifying<br />specific items on an image. With a fresh perspective, the YOLO<br />(You Only Look Once) algorithm was developed in 2015 and<br />performs object detection in a single neural network. That caused<br />the field of object detection to explode and produce considerably<br />more amazing achievements than it had a decade before. So far,<br />YOLO has been improved to eight versions and rated as one<br />of the top object identification algorithms. This is thanks to its<br />combination with many of the most cutting-edge concepts being<br />explored in the computer vision research field. The most recent<br />version of YOLO, known as YOLOv8, performs better than the<br />YOLOv7 and YOLO5 in terms of accuracy and speed, though.<br />This paper examines the most recent developments in computer<br />vision that were incorporated into YOLOv5,YOLO7 and YOLO8<br />and its predecessors.<br /></span><span class="fontstyle0">Index Terms</span><span class="fontstyle1">—Object Detection, YOLO, Autonomous Vehicles,<br />Inland Waterway Vessels, Bounded Boxes, Neural Network, CNN.</span></p><p><span class="fontstyle1"><br /></span></p><p><strong><span class="fontstyle1">Received: 14 June 2023 </span></strong></p><p><strong><span class="fontstyle1">Accepted: 11 September 2023</span></strong></p><p><strong><span class="fontstyle1">Published: 20 November 2023</span></strong></p> |
| first_indexed | 2024-04-24T23:00:43Z |
| format | Article |
| id | doaj.art-a7f13d2d1522462681275fab3d693556 |
| institution | Directory Open Access Journal |
| issn | 2735-5977 2735-5985 |
| language | English |
| last_indexed | 2024-04-24T23:00:43Z |
| publishDate | 2023-11-01 |
| publisher | Academy Publishing Center |
| record_format | Article |
| series | Advances in Computing and Engineering |
| spelling | doaj.art-a7f13d2d1522462681275fab3d6935562024-03-17T15:34:15ZengAcademy Publishing CenterAdvances in Computing and Engineering2735-59772735-59852023-11-01326411710.21622/ACE.2023.03.2.064310Object detection in inland vessels using combined trained and pretrained models of YOLO8Ahmad A. Goudah0Maximilian JarofkaMohmed El-HabroukDieter SchrammYasser G. DessoukyEng. Ahmad Abdullatif Goudah, MSc Computer Eng., Ph.D. Student Data Scientist, Web Developer, and System Engineer in Information and Documentation Center. IT Solutions Consultant. TISC,TTO, and GICO Officer - TICO Office IT Manager for Renewable Energy and Sustainable Development Journal (APC.aast.edu/ojs) Deanery of Scientific Research. Arab Academy for Science, Technology and Maritime Transport (AASTMT) P.O. BOX 1029, Alexandria, Egypt. Tel.: (+2) 03 561 1818 Fax: (+2) 03 562 1022 Website: www.aast.edu <https://www.aast.edu/> e-mail: ahmad.goudah@aast.edu ahmad.goudah@gmail.com<p><span class="fontstyle0">Abstract</span><span class="fontstyle1">—One of the main challenges in computer vision is<br />object detection, which entails both locating and identifying<br />specific items on an image. With a fresh perspective, the YOLO<br />(You Only Look Once) algorithm was developed in 2015 and<br />performs object detection in a single neural network. That caused<br />the field of object detection to explode and produce considerably<br />more amazing achievements than it had a decade before. So far,<br />YOLO has been improved to eight versions and rated as one<br />of the top object identification algorithms. This is thanks to its<br />combination with many of the most cutting-edge concepts being<br />explored in the computer vision research field. The most recent<br />version of YOLO, known as YOLOv8, performs better than the<br />YOLOv7 and YOLO5 in terms of accuracy and speed, though.<br />This paper examines the most recent developments in computer<br />vision that were incorporated into YOLOv5,YOLO7 and YOLO8<br />and its predecessors.<br /></span><span class="fontstyle0">Index Terms</span><span class="fontstyle1">—Object Detection, YOLO, Autonomous Vehicles,<br />Inland Waterway Vessels, Bounded Boxes, Neural Network, CNN.</span></p><p><span class="fontstyle1"><br /></span></p><p><strong><span class="fontstyle1">Received: 14 June 2023 </span></strong></p><p><strong><span class="fontstyle1">Accepted: 11 September 2023</span></strong></p><p><strong><span class="fontstyle1">Published: 20 November 2023</span></strong></p>http://apc.aast.edu/ojs/index.php/ACE/article/view/669 |
| spellingShingle | Ahmad A. Goudah Maximilian Jarofka Mohmed El-Habrouk Dieter Schramm Yasser G. Dessouky Object detection in inland vessels using combined trained and pretrained models of YOLO8 Advances in Computing and Engineering |
| title | Object detection in inland vessels using combined trained and pretrained models of YOLO8 |
| title_full | Object detection in inland vessels using combined trained and pretrained models of YOLO8 |
| title_fullStr | Object detection in inland vessels using combined trained and pretrained models of YOLO8 |
| title_full_unstemmed | Object detection in inland vessels using combined trained and pretrained models of YOLO8 |
| title_short | Object detection in inland vessels using combined trained and pretrained models of YOLO8 |
| title_sort | object detection in inland vessels using combined trained and pretrained models of yolo8 |
| url | http://apc.aast.edu/ojs/index.php/ACE/article/view/669 |
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