Supervised pyramid network based on semantic consistency for object detection
Feature pyramid network is widely used in image understanding tasks based on multi-scale feature learning. The latest multi-scale feature learning focuses on the interactive integration of features in semantic features and detail features. Feature pyramid network complements multi-scale information...
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EDP Sciences
2024-10-01
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| Series: | Xibei Gongye Daxue Xuebao |
| Subjects: | |
| Online Access: | https://www.jnwpu.org/articles/jnwpu/full_html/2024/05/jnwpu2024425p959/jnwpu2024425p959.html |
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| _version_ | 1846125795488038912 |
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| author | DAI Rui XU Pengyue LI Jie HE Lihuo |
| author_facet | DAI Rui XU Pengyue LI Jie HE Lihuo |
| author_sort | DAI Rui |
| collection | DOAJ |
| description | Feature pyramid network is widely used in image understanding tasks based on multi-scale feature learning. The latest multi-scale feature learning focuses on the interactive integration of features in semantic features and detail features. Feature pyramid network complements multi-scale information semantic features and detail features through feature interpolation and summation of adjacent layers. Due to the existence of nonlinear operation and convolution layers with different output dimensions, the relationship among different levels is much more complex, and pixel by pixel summation is suboptimal method. A supervised feature pyramid network based on semantic consistency for object detection is proposed. The present method is composed of asymmetric convolution lateral connection and multi-scale semantic features augmentation. The asymmetric convolution lateral connection improves the generalization of features to various pose objects by learning the feature maps of different receptive fields. The multi-scale semantic features augmentation network improves the detail expression ability of high-level features by supplementing the low-level information for the high-level feature map. Moreover, the present method can provide a better trade-off between accuracy and detection performance. Experiments conduct on the MSCOCO dataset, and the results show that the proposed object detection method's accuracy is improved by 2.6% without increasing extra FLOPs. |
| format | Article |
| id | doaj-art-de773c3fa1a24f96bf3fb41ee3b73d53 |
| institution | Kabale University |
| issn | 1000-2758 2609-7125 |
| language | zho |
| publishDate | 2024-10-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Xibei Gongye Daxue Xuebao |
| spelling | doaj-art-de773c3fa1a24f96bf3fb41ee3b73d532024-12-13T10:05:05ZzhoEDP SciencesXibei Gongye Daxue Xuebao1000-27582609-71252024-10-0142595996810.1051/jnwpu/20244250959jnwpu2024425p959Supervised pyramid network based on semantic consistency for object detectionDAI Rui0XU Pengyue1LI Jie2HE Lihuo3School of Electronic Engineering, Xidian UniversitySchool of Electronic Engineering, Xidian UniversitySchool of Electronic Engineering, Xidian UniversitySchool of Electronic Engineering, Xidian UniversityFeature pyramid network is widely used in image understanding tasks based on multi-scale feature learning. The latest multi-scale feature learning focuses on the interactive integration of features in semantic features and detail features. Feature pyramid network complements multi-scale information semantic features and detail features through feature interpolation and summation of adjacent layers. Due to the existence of nonlinear operation and convolution layers with different output dimensions, the relationship among different levels is much more complex, and pixel by pixel summation is suboptimal method. A supervised feature pyramid network based on semantic consistency for object detection is proposed. The present method is composed of asymmetric convolution lateral connection and multi-scale semantic features augmentation. The asymmetric convolution lateral connection improves the generalization of features to various pose objects by learning the feature maps of different receptive fields. The multi-scale semantic features augmentation network improves the detail expression ability of high-level features by supplementing the low-level information for the high-level feature map. Moreover, the present method can provide a better trade-off between accuracy and detection performance. Experiments conduct on the MSCOCO dataset, and the results show that the proposed object detection method's accuracy is improved by 2.6% without increasing extra FLOPs.https://www.jnwpu.org/articles/jnwpu/full_html/2024/05/jnwpu2024425p959/jnwpu2024425p959.html目标检测语义一致性特征金字塔网络 |
| spellingShingle | DAI Rui XU Pengyue LI Jie HE Lihuo Supervised pyramid network based on semantic consistency for object detection Xibei Gongye Daxue Xuebao 目标检测 语义一致性 特征金字塔网络 |
| title | Supervised pyramid network based on semantic consistency for object detection |
| title_full | Supervised pyramid network based on semantic consistency for object detection |
| title_fullStr | Supervised pyramid network based on semantic consistency for object detection |
| title_full_unstemmed | Supervised pyramid network based on semantic consistency for object detection |
| title_short | Supervised pyramid network based on semantic consistency for object detection |
| title_sort | supervised pyramid network based on semantic consistency for object detection |
| topic | 目标检测 语义一致性 特征金字塔网络 |
| url | https://www.jnwpu.org/articles/jnwpu/full_html/2024/05/jnwpu2024425p959/jnwpu2024425p959.html |
| work_keys_str_mv | AT dairui supervisedpyramidnetworkbasedonsemanticconsistencyforobjectdetection AT xupengyue supervisedpyramidnetworkbasedonsemanticconsistencyforobjectdetection AT lijie supervisedpyramidnetworkbasedonsemanticconsistencyforobjectdetection AT helihuo supervisedpyramidnetworkbasedonsemanticconsistencyforobjectdetection |