Surveillance in Maritime Scenario using Deep-Learning and Swarm Intelligence
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
| المؤلفون الرئيسيون: | , , , , |
|---|---|
| مؤلفون آخرون: | |
| التنسيق: | أطروحة |
| اللغة: | en_US |
| منشور في: |
Brac University
2022
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10361/17649 |
| id |
10361-17649 |
|---|---|
| record_format |
dspace |
| spelling |
10361-176492022-12-14T21:01:38Z Surveillance in Maritime Scenario using Deep-Learning and Swarm Intelligence Islam, Nazmul Rahman Bhuiya, MD. Samiur Drishty, Ayesha Siddiqua Saha, Snigdha Suparna Akash, Utsha Das Chakrabarty, Dr. Amitabha Department of Computer Science and Engineering, Brac University Object Detection Marine Search and Rescue (SAR) Unmanned Aerial Vehicles (UAV) Convolutional Neural Network Swarm Intelligence System safety. Swarm intelligence. Deep learning (Machine learning) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 53-57). Unmanned Aerial Vehicles (UAVs) have played a crucial role in supporting Search and Rescue (SAR) Operations due to their fast movement capabilities and flexibil ity. During a search and rescue operation scenario, the time constraint is a crucial parameter, so the required time to detect humans in distress with precision is also a vital part. Modern Deep-learning algorithms like CNN also aid in these missions. However, most models and datasets available focus on search and rescue missions on the ground or land. UAV-based search and rescue operations in the Maritime Scenario remain a challenge. This study focused on using deep learning algorithms such as CNN to precisely detect a human in peril with a swarm of drones. At the same time, we emphasize using swarm intelligence algorithms such as Particle Swarm Algorithm (PSO) to effectively find a victim in the shortest time by ex ploring a massive area. The distinctiveness of this system is that it combines the model with the best Accuracy to detect and the best swarm intelligence algorithm for finding targets in the quickest time possible, thus enhancing the surveillance mission. In this research, among VGG16, ResNet50V2, InceptionV3, Xception and MobileNetv2 models, VGG16 produced IoU (Intersection over Union) score of 0.62 with Class Label accuracy of 99.15% and Bounding Box accuracy of 88.74% in CNN part. Along with that, among three different swarm intelligence algorithms, accord ing to the simulation, Particle Swarm Optimization Algorithm took the minimum average time which is 20.4 units, whereas the Grey Wolf Optimization algorithm and Bat Optimization Algorithm, respectively took 65.6 and 73.8 unit of time. Nazmul Islam MD. Samiur Rahman Bhuiya Ayesha Siddiqua Drishty Snigdha Suparna Saha Utsha Das Akash B. Computer Science 2022-12-14T08:08:32Z 2022-12-14T08:08:32Z 2022 2022-05 Thesis ID: 18101321 ID: 18101584 ID: 18101674 ID: 18101385 ID: 18101322 http://hdl.handle.net/10361/17649 en_US 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. 57 Pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
en_US |
| topic |
Object Detection Marine Search and Rescue (SAR) Unmanned Aerial Vehicles (UAV) Convolutional Neural Network Swarm Intelligence System safety. Swarm intelligence. Deep learning (Machine learning) |
| spellingShingle |
Object Detection Marine Search and Rescue (SAR) Unmanned Aerial Vehicles (UAV) Convolutional Neural Network Swarm Intelligence System safety. Swarm intelligence. Deep learning (Machine learning) Islam, Nazmul Rahman Bhuiya, MD. Samiur Drishty, Ayesha Siddiqua Saha, Snigdha Suparna Akash, Utsha Das Surveillance in Maritime Scenario using Deep-Learning and Swarm Intelligence |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Chakrabarty, Dr. Amitabha |
| author_facet |
Chakrabarty, Dr. Amitabha Islam, Nazmul Rahman Bhuiya, MD. Samiur Drishty, Ayesha Siddiqua Saha, Snigdha Suparna Akash, Utsha Das |
| format |
Thesis |
| author |
Islam, Nazmul Rahman Bhuiya, MD. Samiur Drishty, Ayesha Siddiqua Saha, Snigdha Suparna Akash, Utsha Das |
| author_sort |
Islam, Nazmul |
| title |
Surveillance in Maritime Scenario using Deep-Learning and Swarm Intelligence |
| title_short |
Surveillance in Maritime Scenario using Deep-Learning and Swarm Intelligence |
| title_full |
Surveillance in Maritime Scenario using Deep-Learning and Swarm Intelligence |
| title_fullStr |
Surveillance in Maritime Scenario using Deep-Learning and Swarm Intelligence |
| title_full_unstemmed |
Surveillance in Maritime Scenario using Deep-Learning and Swarm Intelligence |
| title_sort |
surveillance in maritime scenario using deep-learning and swarm intelligence |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/17649 |
| work_keys_str_mv |
AT islamnazmul surveillanceinmaritimescenariousingdeeplearningandswarmintelligence AT rahmanbhuiyamdsamiur surveillanceinmaritimescenariousingdeeplearningandswarmintelligence AT drishtyayeshasiddiqua surveillanceinmaritimescenariousingdeeplearningandswarmintelligence AT sahasnigdhasuparna surveillanceinmaritimescenariousingdeeplearningandswarmintelligence AT akashutshadas surveillanceinmaritimescenariousingdeeplearningandswarmintelligence |
| _version_ |
1814309057465417728 |