Accurate and fast single shot multibox detector
With the development of deep learning, the performance of object detection has made great progress. However, there are still some challenging problems, such as the detection accuracy of small objects and the efficiency of the detector. This study proposes an accurate and fast single shot multibox de...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-09-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2019.0711 |
| _version_ | 1797684747281891328 |
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| author | Lie Guo Dongxing Wang Linhui Li Jindun Feng |
| author_facet | Lie Guo Dongxing Wang Linhui Li Jindun Feng |
| author_sort | Lie Guo |
| collection | DOAJ |
| description | With the development of deep learning, the performance of object detection has made great progress. However, there are still some challenging problems, such as the detection accuracy of small objects and the efficiency of the detector. This study proposes an accurate and fast single shot multibox detector, which includes context comprehensive enhancement (CCE) module and feature enhancement module (FEM). To integrate more efficient information when aggregating context information, the conv4_3 and fc_7 feature maps are merged to design the CCE module. To obtain more fine‐grained feature information, this study presents a FEM and special feature enhancement module (FEM‐s) module that can fuse different receptive field sizes to better adapt to the scale change of the object. Compared to existing methods based on deep learning, the proposed method helps to gradually produce more detailed feature maps with better performance. Under the premise of ensuring real‐time speed, the authors network can achieve 81.2 mean average precision on the PASCAL VOC 2007 test with an input size of 320 × 320 on a single Nvidia 2080Ti GPU. |
| first_indexed | 2024-03-12T00:34:16Z |
| format | Article |
| id | doaj.art-8e5355a325c4490386a04d256a67132e |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:34:16Z |
| publishDate | 2020-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-8e5355a325c4490386a04d256a67132e2023-09-15T10:06:49ZengWileyIET Computer Vision1751-96321751-96402020-09-0114639139810.1049/iet-cvi.2019.0711Accurate and fast single shot multibox detectorLie Guo0Dongxing Wang1Linhui Li2Jindun Feng3School of Automotive EngineeringDalian University of TechnologyDalian116024People's Republic of ChinaSchool of Automotive EngineeringDalian University of TechnologyDalian116024People's Republic of ChinaSchool of Automotive EngineeringDalian University of TechnologyDalian116024People's Republic of ChinaSchool of Automotive EngineeringDalian University of TechnologyDalian116024People's Republic of ChinaWith the development of deep learning, the performance of object detection has made great progress. However, there are still some challenging problems, such as the detection accuracy of small objects and the efficiency of the detector. This study proposes an accurate and fast single shot multibox detector, which includes context comprehensive enhancement (CCE) module and feature enhancement module (FEM). To integrate more efficient information when aggregating context information, the conv4_3 and fc_7 feature maps are merged to design the CCE module. To obtain more fine‐grained feature information, this study presents a FEM and special feature enhancement module (FEM‐s) module that can fuse different receptive field sizes to better adapt to the scale change of the object. Compared to existing methods based on deep learning, the proposed method helps to gradually produce more detailed feature maps with better performance. Under the premise of ensuring real‐time speed, the authors network can achieve 81.2 mean average precision on the PASCAL VOC 2007 test with an input size of 320 × 320 on a single Nvidia 2080Ti GPU.https://doi.org/10.1049/iet-cvi.2019.0711fast single shot multibox detectordeep learningobject detectionfeature enhancement modulefc_7 feature mapsCCE module |
| spellingShingle | Lie Guo Dongxing Wang Linhui Li Jindun Feng Accurate and fast single shot multibox detector IET Computer Vision fast single shot multibox detector deep learning object detection feature enhancement module fc_7 feature maps CCE module |
| title | Accurate and fast single shot multibox detector |
| title_full | Accurate and fast single shot multibox detector |
| title_fullStr | Accurate and fast single shot multibox detector |
| title_full_unstemmed | Accurate and fast single shot multibox detector |
| title_short | Accurate and fast single shot multibox detector |
| title_sort | accurate and fast single shot multibox detector |
| topic | fast single shot multibox detector deep learning object detection feature enhancement module fc_7 feature maps CCE module |
| url | https://doi.org/10.1049/iet-cvi.2019.0711 |
| work_keys_str_mv | AT lieguo accurateandfastsingleshotmultiboxdetector AT dongxingwang accurateandfastsingleshotmultiboxdetector AT linhuili accurateandfastsingleshotmultiboxdetector AT jindunfeng accurateandfastsingleshotmultiboxdetector |