Classification of Non-Conventional Ships Using a Neural Bag-Of-Words Mechanism
The existing methods for monitoring vessels are mainly based on radar and automatic identification systems. Additional sensors that are used include video cameras. Such systems feature cameras that capture images and software that analyzes the selected video frames. Methods for the classification of...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/6/1608 |
| _version_ | 1798039219758694400 |
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| author | Dawid Polap Marta Wlodarczyk-Sielicka |
| author_facet | Dawid Polap Marta Wlodarczyk-Sielicka |
| author_sort | Dawid Polap |
| collection | DOAJ |
| description | The existing methods for monitoring vessels are mainly based on radar and automatic identification systems. Additional sensors that are used include video cameras. Such systems feature cameras that capture images and software that analyzes the selected video frames. Methods for the classification of non-conventional vessels are not widely known. These methods, based on image samples, can be considered difficult. This paper is intended to show an alternative way to approach image classification problems; not by classifying the entire input data, but smaller parts. The described solution is based on splitting the image of a ship into smaller parts and classifying them into vectors that can be identified as features using a convolutional neural network (CNN). This idea is a representation of a bag-of-words mechanism, where created feature vectors might be called words, and by using them a solution can assign images a specific class. As part of the experiment, the authors performed two tests. In the first, two classes were analyzed and the results obtained show great potential for application. In the second, the authors used much larger sets of images belonging to five vessel types. The proposed method indeed improved the results of classic approaches by 5%. The paper shows an alternative approach for the classification of non-conventional vessels to increase accuracy. |
| first_indexed | 2024-04-11T21:50:53Z |
| format | Article |
| id | doaj.art-eef875943cfa4c59baf72d56abf05169 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:50:53Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-eef875943cfa4c59baf72d56abf051692022-12-22T04:01:15ZengMDPI AGSensors1424-82202020-03-01206160810.3390/s20061608s20061608Classification of Non-Conventional Ships Using a Neural Bag-Of-Words MechanismDawid Polap0Marta Wlodarczyk-Sielicka1Marine Technology Ltd., 81-521 Gdynia, PolandDepartment of Navigation, Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, PolandThe existing methods for monitoring vessels are mainly based on radar and automatic identification systems. Additional sensors that are used include video cameras. Such systems feature cameras that capture images and software that analyzes the selected video frames. Methods for the classification of non-conventional vessels are not widely known. These methods, based on image samples, can be considered difficult. This paper is intended to show an alternative way to approach image classification problems; not by classifying the entire input data, but smaller parts. The described solution is based on splitting the image of a ship into smaller parts and classifying them into vectors that can be identified as features using a convolutional neural network (CNN). This idea is a representation of a bag-of-words mechanism, where created feature vectors might be called words, and by using them a solution can assign images a specific class. As part of the experiment, the authors performed two tests. In the first, two classes were analyzed and the results obtained show great potential for application. In the second, the authors used much larger sets of images belonging to five vessel types. The proposed method indeed improved the results of classic approaches by 5%. The paper shows an alternative approach for the classification of non-conventional vessels to increase accuracy.https://www.mdpi.com/1424-8220/20/6/1608bag-of-words mechanismmachine learningimage analysisship classificationmarine systemriver monitoring systemfeature extraction |
| spellingShingle | Dawid Polap Marta Wlodarczyk-Sielicka Classification of Non-Conventional Ships Using a Neural Bag-Of-Words Mechanism Sensors bag-of-words mechanism machine learning image analysis ship classification marine system river monitoring system feature extraction |
| title | Classification of Non-Conventional Ships Using a Neural Bag-Of-Words Mechanism |
| title_full | Classification of Non-Conventional Ships Using a Neural Bag-Of-Words Mechanism |
| title_fullStr | Classification of Non-Conventional Ships Using a Neural Bag-Of-Words Mechanism |
| title_full_unstemmed | Classification of Non-Conventional Ships Using a Neural Bag-Of-Words Mechanism |
| title_short | Classification of Non-Conventional Ships Using a Neural Bag-Of-Words Mechanism |
| title_sort | classification of non conventional ships using a neural bag of words mechanism |
| topic | bag-of-words mechanism machine learning image analysis ship classification marine system river monitoring system feature extraction |
| url | https://www.mdpi.com/1424-8220/20/6/1608 |
| work_keys_str_mv | AT dawidpolap classificationofnonconventionalshipsusinganeuralbagofwordsmechanism AT martawlodarczyksielicka classificationofnonconventionalshipsusinganeuralbagofwordsmechanism |