Leveraging robust CNN architectures for real-time object recognition from conveyor belt
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
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| Định dạng: | Luận văn |
| Ngôn ngữ: | English |
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Brac University
2023
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| Truy cập trực tuyến: | http://hdl.handle.net/10361/19296 |
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10361-19296 |
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10361-192962023-08-06T21:02:03Z Leveraging robust CNN architectures for real-time object recognition from conveyor belt Moon, Nowrin Tasnim Siddiqua, Sabiha Afrin Parvin, Shahana Muntaha, Sidratul Hassan, K.M. Mehedi Mostakim, Moin Rahman, Rafeed Department of Computer Science and Engineering, Brac University Neural network Object detection Conveyor belt VGG16 ResNet MobileNet YOLOv5 YOLOv7 Convolutional Neural Network CNN Vision transformer Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 44-47). In the innovative era, the problem of recognizing undesirable objects and individuals on conveyor belts is addressed by various architectural or algorithmic approaches. Conveyor belts are those by which things go in a straight line for transportation, and it works as an object-carrying medium. Sometimes some unwanted objects go through the belt mistakenly, which can be very dangerous. Moreover, detection accuracy is much needed to avoid such deadly occurrences. Better accuracy can be achieved by performing detection in real-time. Furthermore, this upgraded system will assist individuals by lowering the danger of any accidents and provide a real-time example of an airport conveyor belt by detecting any unwanted moving objects with the help of a camera sensor and by applying different algorithms and methods of the neural network. Therefore, in this paper, we have implemented a few algorithms that comprise a customized Convolutional Neural Network, YOLOv5, YOLOv7, and Vision Transformer as well as some Transfer learning methods over a few pre-trained models such as VGG16, ResNet50, and MobileNetv2 to produce a better strategy on our customized dataset to boost the accuracy of recognition. Nowrin Tasnim Moon Sabiha Afrin Siddiqua Shahana Parvin Sidratul Muntaha K.M. Mehedi Hassan B. Computer Science 2023-08-06T06:03:19Z 2023-08-06T06:03:19Z 2023 2023-01 Thesis ID: 19101109 ID: 19101050 ID: 19101037 ID: 22241162 ID: 22241188 http://hdl.handle.net/10361/19296 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. 47 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Neural network Object detection Conveyor belt VGG16 ResNet MobileNet YOLOv5 YOLOv7 Convolutional Neural Network CNN Vision transformer Neural networks (Computer science) |
| spellingShingle |
Neural network Object detection Conveyor belt VGG16 ResNet MobileNet YOLOv5 YOLOv7 Convolutional Neural Network CNN Vision transformer Neural networks (Computer science) Moon, Nowrin Tasnim Siddiqua, Sabiha Afrin Parvin, Shahana Muntaha, Sidratul Hassan, K.M. Mehedi Leveraging robust CNN architectures for real-time object recognition from conveyor belt |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. |
| author2 |
Mostakim, Moin |
| author_facet |
Mostakim, Moin Moon, Nowrin Tasnim Siddiqua, Sabiha Afrin Parvin, Shahana Muntaha, Sidratul Hassan, K.M. Mehedi |
| format |
Thesis |
| author |
Moon, Nowrin Tasnim Siddiqua, Sabiha Afrin Parvin, Shahana Muntaha, Sidratul Hassan, K.M. Mehedi |
| author_sort |
Moon, Nowrin Tasnim |
| title |
Leveraging robust CNN architectures for real-time object recognition from conveyor belt |
| title_short |
Leveraging robust CNN architectures for real-time object recognition from conveyor belt |
| title_full |
Leveraging robust CNN architectures for real-time object recognition from conveyor belt |
| title_fullStr |
Leveraging robust CNN architectures for real-time object recognition from conveyor belt |
| title_full_unstemmed |
Leveraging robust CNN architectures for real-time object recognition from conveyor belt |
| title_sort |
leveraging robust cnn architectures for real-time object recognition from conveyor belt |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/19296 |
| work_keys_str_mv |
AT moonnowrintasnim leveragingrobustcnnarchitecturesforrealtimeobjectrecognitionfromconveyorbelt AT siddiquasabihaafrin leveragingrobustcnnarchitecturesforrealtimeobjectrecognitionfromconveyorbelt AT parvinshahana leveragingrobustcnnarchitecturesforrealtimeobjectrecognitionfromconveyorbelt AT muntahasidratul leveragingrobustcnnarchitecturesforrealtimeobjectrecognitionfromconveyorbelt AT hassankmmehedi leveragingrobustcnnarchitecturesforrealtimeobjectrecognitionfromconveyorbelt |
| _version_ |
1814307497971810304 |