Lightweight Mask RCNN for Warship Detection and Segmentation
As the term X(Everything)+AI indicates, AI is applied in every aspect of current societies. Likewise, the military requirements for AI are increasing as well. AIs that automatically detect and classify objects are required for surveillance and reconnaissance. Especially in terms of naval...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9705519/ |
| _version_ | 1831767056530276352 |
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| author | Jinyoung Park Hoseok Moon |
| author_facet | Jinyoung Park Hoseok Moon |
| author_sort | Jinyoung Park |
| collection | DOAJ |
| description | As the term X(Everything)+AI indicates, AI is applied in every aspect of current societies. Likewise, the military requirements for AI are increasing as well. AIs that automatically detect and classify objects are required for surveillance and reconnaissance. Especially in terms of naval operation, identifying types of warships and recognizing mounted armaments have significance as the first step of the operation. This study is the proposal of an AI model that can identify warships’ type and weapon by analyzing video information taken on sea, and evaluate threat priority and response level. The proposed model is based on Mask RCNN, the Image Segmentation model, but was designed in a lightweight version, so that it could be used on a platform of the vessel where the use of high performing computers is limited. To lightweight the model, the former backbone was replaced with MobileNetV2, and the convolution operation of the RPN was replaced with Depthwise Separable Convolution operation, which operates respectively in each channel. The lightweight Mask RCNN model showed 64% lower number of parameters compared to the base model. However, its mAP, the classification accuracy, was similar with the base model. |
| first_indexed | 2024-12-22T06:35:22Z |
| format | Article |
| id | doaj.art-227334eb336f41159c695937b45913e7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T06:35:22Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-227334eb336f41159c695937b45913e72022-12-21T18:35:36ZengIEEEIEEE Access2169-35362022-01-0110249362494410.1109/ACCESS.2022.31492979705519Lightweight Mask RCNN for Warship Detection and SegmentationJinyoung Park0https://orcid.org/0000-0002-3953-024XHoseok Moon1https://orcid.org/0000-0003-4697-0750Department of Military Science, Korea National Defense University, Nonsan, South KoreaDepartment of Military Science, Korea National Defense University, Nonsan, South KoreaAs the term X(Everything)+AI indicates, AI is applied in every aspect of current societies. Likewise, the military requirements for AI are increasing as well. AIs that automatically detect and classify objects are required for surveillance and reconnaissance. Especially in terms of naval operation, identifying types of warships and recognizing mounted armaments have significance as the first step of the operation. This study is the proposal of an AI model that can identify warships’ type and weapon by analyzing video information taken on sea, and evaluate threat priority and response level. The proposed model is based on Mask RCNN, the Image Segmentation model, but was designed in a lightweight version, so that it could be used on a platform of the vessel where the use of high performing computers is limited. To lightweight the model, the former backbone was replaced with MobileNetV2, and the convolution operation of the RPN was replaced with Depthwise Separable Convolution operation, which operates respectively in each channel. The lightweight Mask RCNN model showed 64% lower number of parameters compared to the base model. However, its mAP, the classification accuracy, was similar with the base model.https://ieeexplore.ieee.org/document/9705519/Warship detection and segmentationlightweight deep learningMask RCNNMobileNet |
| spellingShingle | Jinyoung Park Hoseok Moon Lightweight Mask RCNN for Warship Detection and Segmentation IEEE Access Warship detection and segmentation lightweight deep learning Mask RCNN MobileNet |
| title | Lightweight Mask RCNN for Warship Detection and Segmentation |
| title_full | Lightweight Mask RCNN for Warship Detection and Segmentation |
| title_fullStr | Lightweight Mask RCNN for Warship Detection and Segmentation |
| title_full_unstemmed | Lightweight Mask RCNN for Warship Detection and Segmentation |
| title_short | Lightweight Mask RCNN for Warship Detection and Segmentation |
| title_sort | lightweight mask rcnn for warship detection and segmentation |
| topic | Warship detection and segmentation lightweight deep learning Mask RCNN MobileNet |
| url | https://ieeexplore.ieee.org/document/9705519/ |
| work_keys_str_mv | AT jinyoungpark lightweightmaskrcnnforwarshipdetectionandsegmentation AT hoseokmoon lightweightmaskrcnnforwarshipdetectionandsegmentation |