Object Detection Algorithm Based on Feature Enhancement and Anchor-object Matching
In order to improve the detection accuracy of SSD (Single Shot MultiBox Detector), an A-SSD (Anchor-object SSD) object detection algorithm based on Anchor-object matching is proposed. In the feature extraction part of the algorithm, parallel convolution and hole convolution are used to form the rece...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2022-06-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2098 |
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| author | LI Cheng-yan ZHAO Shuai CHE Zi-xuan |
| author_facet | LI Cheng-yan ZHAO Shuai CHE Zi-xuan |
| author_sort | LI Cheng-yan |
| collection | DOAJ |
| description | In order to improve the detection accuracy of SSD (Single Shot MultiBox Detector), an A-SSD (Anchor-object SSD) object detection algorithm based on Anchor-object matching is proposed. In the feature extraction part of the algorithm, parallel convolution and hole convolution are used to form the receptive field module, which increases the receptive field of the feature map and obtains multi-scale feature information, combining the shallow features containing texture, edge and other detailed information with rich fusion of deep features of semantic information. In the detector part of the algorithm, the Anchor-object matching method combined with the SSD multi-layer feature map is used to construct the corresponding Anchor package for each detection target. Through the selection-suppression optimization strategy, the Anchor with higher confidence is selected to update the model. Anchor evaluation score, in continuous iterative learning, the model parameters, Anchor position coordinates and classification confidence are continuously optimized. The mAP of the A-SSD algorithm on the PASCAL VOC data set reached 80.7, and the missed detection rate of the A-SSD algorithm on the workshop pedestrian data set was 3.5%, and the accuracy rate was 91.5%. |
| format | Article |
| id | doaj-art-063069dcd24b436ca0766896833d8a98 |
| institution | DOAJ |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2022-06-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-063069dcd24b436ca0766896833d8a982025-08-20T03:14:35ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832022-06-012703738110.15938/j.jhust.2022.03.010Object Detection Algorithm Based on Feature Enhancement and Anchor-object MatchingLI Cheng-yan0ZHAO Shuai1CHE Zi-xuan2School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaIn order to improve the detection accuracy of SSD (Single Shot MultiBox Detector), an A-SSD (Anchor-object SSD) object detection algorithm based on Anchor-object matching is proposed. In the feature extraction part of the algorithm, parallel convolution and hole convolution are used to form the receptive field module, which increases the receptive field of the feature map and obtains multi-scale feature information, combining the shallow features containing texture, edge and other detailed information with rich fusion of deep features of semantic information. In the detector part of the algorithm, the Anchor-object matching method combined with the SSD multi-layer feature map is used to construct the corresponding Anchor package for each detection target. Through the selection-suppression optimization strategy, the Anchor with higher confidence is selected to update the model. Anchor evaluation score, in continuous iterative learning, the model parameters, Anchor position coordinates and classification confidence are continuously optimized. The mAP of the A-SSD algorithm on the PASCAL VOC data set reached 80.7, and the missed detection rate of the A-SSD algorithm on the workshop pedestrian data set was 3.5%, and the accuracy rate was 91.5%.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2098object detectionssd algorithmfeature extractiondetectoranchor-object matching |
| spellingShingle | LI Cheng-yan ZHAO Shuai CHE Zi-xuan Object Detection Algorithm Based on Feature Enhancement and Anchor-object Matching Journal of Harbin University of Science and Technology object detection ssd algorithm feature extraction detector anchor-object matching |
| title | Object Detection Algorithm Based on Feature Enhancement and Anchor-object Matching |
| title_full | Object Detection Algorithm Based on Feature Enhancement and Anchor-object Matching |
| title_fullStr | Object Detection Algorithm Based on Feature Enhancement and Anchor-object Matching |
| title_full_unstemmed | Object Detection Algorithm Based on Feature Enhancement and Anchor-object Matching |
| title_short | Object Detection Algorithm Based on Feature Enhancement and Anchor-object Matching |
| title_sort | object detection algorithm based on feature enhancement and anchor object matching |
| topic | object detection ssd algorithm feature extraction detector anchor-object matching |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2098 |
| work_keys_str_mv | AT lichengyan objectdetectionalgorithmbasedonfeatureenhancementandanchorobjectmatching AT zhaoshuai objectdetectionalgorithmbasedonfeatureenhancementandanchorobjectmatching AT chezixuan objectdetectionalgorithmbasedonfeatureenhancementandanchorobjectmatching |