Fire and disaster detection with multimodal quadcopter By machine learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
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| Lingua: | English |
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Brac University
2023
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| Accesso online: | http://hdl.handle.net/10361/20208 |
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10361-20208 |
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10361-202082023-08-30T21:02:28Z Fire and disaster detection with multimodal quadcopter By machine learning Afrin, Anika Rahman, Md Moshiour Chowdhury, Ayash Hossain Eshraq, Mirza Ukasha, Mehvish Rahman Rahman, Khalilur Department of Computer Science and Engineering, Brac University YOLOV5 YOLOV7 Fire detection Disaster detection Sound detection Mapping MiDaS V3 PIX4D mapper Machine learning Quadcopter This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 79-82). Our thesis research is consisted of developing a model that can detect early fires and, mapping the area for fire and disaster detection using a UAV (Unmanned Aerial Vehicle) or quadcopter if a fire break out. Furthermore, MLP uses fire or no fire detection, sound analysis, and input sensor to create a multimodal system architecture. First, surveillance cameras detects the early stages of a fire using luminous smoke and textured flame. However, if the fire has already started, an alarm will sound, activating the quadcopter operation. Due to the quadcopter’s camera and sound system input, it obtains an aerial perspective and maps the fireaffected region while indicating human life. Finally, disaster detection provides us with a map indicating the safe zone where the less damaged part of the building will assist the fire department in saving human lives. The unique aspect of our thesis is that it designs a complete fire detection and rescue model. It will effectively detect a fire before an incident occurs and map the fire-affected region after the incident with human life signs and the safest path to rescue. The main goal here is to prevent or mitigate damage by immediately alerting the fire department. We have collected primary dataset of Fire and Disaster. Moreover, we increased the accuracy of our fire dataset to 80.32% and increased the accuracy of our disaster dataset to 9.2%. We tried to reduce the false detection of fire. Added to that, we have integrated all the five models in graphical user interface. Anika Afrin Md Moshiour Rahman Ayash Hossain Chowdhury Mirza Eshraq Mehvish Rahman Ukasha B. Computer Science 2023-08-30T05:01:22Z 2023-08-30T05:01:22Z 2023 2023-03 Thesis ID 19301072 ID 20101096 ID 20101095 ID 20101094 ID 20101097 http://hdl.handle.net/10361/20208 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 82 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
YOLOV5 YOLOV7 Fire detection Disaster detection Sound detection Mapping MiDaS V3 PIX4D mapper Machine learning Quadcopter |
| spellingShingle |
YOLOV5 YOLOV7 Fire detection Disaster detection Sound detection Mapping MiDaS V3 PIX4D mapper Machine learning Quadcopter Afrin, Anika Rahman, Md Moshiour Chowdhury, Ayash Hossain Eshraq, Mirza Ukasha, Mehvish Rahman Fire and disaster detection with multimodal quadcopter By machine learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. |
| author2 |
Rahman, Khalilur |
| author_facet |
Rahman, Khalilur Afrin, Anika Rahman, Md Moshiour Chowdhury, Ayash Hossain Eshraq, Mirza Ukasha, Mehvish Rahman |
| format |
Thesis |
| author |
Afrin, Anika Rahman, Md Moshiour Chowdhury, Ayash Hossain Eshraq, Mirza Ukasha, Mehvish Rahman |
| author_sort |
Afrin, Anika |
| title |
Fire and disaster detection with multimodal quadcopter By machine learning |
| title_short |
Fire and disaster detection with multimodal quadcopter By machine learning |
| title_full |
Fire and disaster detection with multimodal quadcopter By machine learning |
| title_fullStr |
Fire and disaster detection with multimodal quadcopter By machine learning |
| title_full_unstemmed |
Fire and disaster detection with multimodal quadcopter By machine learning |
| title_sort |
fire and disaster detection with multimodal quadcopter by machine learning |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/20208 |
| work_keys_str_mv |
AT afrinanika fireanddisasterdetectionwithmultimodalquadcopterbymachinelearning AT rahmanmdmoshiour fireanddisasterdetectionwithmultimodalquadcopterbymachinelearning AT chowdhuryayashhossain fireanddisasterdetectionwithmultimodalquadcopterbymachinelearning AT eshraqmirza fireanddisasterdetectionwithmultimodalquadcopterbymachinelearning AT ukashamehvishrahman fireanddisasterdetectionwithmultimodalquadcopterbymachinelearning |
| _version_ |
1814307009270382592 |