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Normal
0
false
false
false
EN-ID
ZH-CN
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<style>
/* Style Definitions */
table.MsoNormalTable
{mso-style-name:"Table Normal";
mso-tstyle-rowband-size:0;
mso-tstyle-colband-size:0;
mso-style-noshow:yes;
mso-style-priority:99;
mso-style-parent:"";
mso-padding-alt:0cm 5.4pt 0cm 5.4pt;
mso-para-margin-top:0cm;
mso-para-margin-right:0cm;
mso-para-margin-bottom:8.0pt;
mso-para-margin-left:0cm;
line-height:107%;
mso-pagination:widow-orphan;
font-size:11.0pt;
mso-bidi-font-size:10.0pt;
font-family:"Calibri",sans-serif;
mso-ascii-font-family:Calibri;
mso-ascii-theme-font:minor-latin;
mso-hansi-font-family:Calibri;
mso-hansi-theme-font:minor-latin;
mso-bidi-font-family:Mangal;
mso-bidi-theme-font:minor-bidi;
mso-fareast-language:EN-US;
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<![endif]-->The era of technology today is experiencing rapid
development, the utilization of digital image processing is not a new concept.
Digital image processing involves the manipulation of images using computers to
generate information that can be further processed, formed, or analyzed by
humans. This process can be used to identify objects based on specific features.
This Final Project explains the application of digital image processing in the drone
Ryze Tello to detect fire using YOLOv8 and OpenCV methods, with the programming
language used is Python. The YOLOv8 method detects fire using the best data
(best) as the basis for classification. This best data is produced from the
training process of various datasets related to fire objects. On the other
hand, in the OpenCV method, fire objects are detected based on a specific
reference color, it is reddish-yellow. This research is conducted to compare
the effectiveness of both approaches in detecting fires under various
conditions.">
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false
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<style>
/* Style Definitions */
table.MsoNormalTable
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mso-para-margin-top:0cm;
mso-para-margin-right:0cm;
mso-para-margin-bottom:8.0pt;
mso-para-margin-left:0cm;
line-height:107%;
mso-pagination:widow-orphan;
font-size:11.0pt;
mso-bidi-font-size:10.0pt;
font-family:"Calibri",sans-serif;
mso-ascii-font-family:Calibri;
mso-ascii-theme-font:minor-latin;
mso-hansi-font-family:Calibri;
mso-hansi-theme-font:minor-latin;
mso-bidi-font-family:Mangal;
mso-bidi-theme-font:minor-bidi;
mso-fareast-language:EN-US;
mso-bidi-language:HI;}
</style>
<![endif]-->The era of technology today is experiencing rapid
development, the utilization of digital image processing is not a new concept.
Digital image processing involves the manipulation of images using computers to
generate information that can be further processed, formed, or analyzed by
humans. This process can be used to identify objects based on specific features.
This Final Project explains the application of digital image processing in the drone
Ryze Tello to detect fire using YOLOv8 and OpenCV methods, with the programming
language used is Python. The YOLOv8 method detects fire using the best data
(best) as the basis for classification. This best data is produced from the
training process of various datasets related to fire objects. On the other
hand, in the OpenCV method, fire objects are detected based on a specific
reference color, it is reddish-yellow. This research is conducted to compare
the effectiveness of both approaches in detecting fires under various
conditions.">
