Multilevel receptive field expansion network for small object detection
Abstract Small object detection remains a bottleneck because there is little visual information about them, especially in the deep layers. To improve the detection performance of small objects, here, Swin Transformer is introduced as the model backbone network to extract rich features of small objec...
Main Authors: | , , , , |
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Format: | Article |
Language: | English |
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Wiley
2023-06-01
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Series: | IET Image Processing |
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Online Access: | https://doi.org/10.1049/ipr2.12799 |
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author | Zhiwei Liu Menghan Gan Li Xiong Xiaofeng Mao Yue Que |
author_facet | Zhiwei Liu Menghan Gan Li Xiong Xiaofeng Mao Yue Que |
author_sort | Zhiwei Liu |
collection | DOAJ |
description | Abstract Small object detection remains a bottleneck because there is little visual information about them, especially in the deep layers. To improve the detection performance of small objects, here, Swin Transformer is introduced as the model backbone network to extract rich features of small objects. Then, a multilevel receptive field expansion network (MRFENet) is proposed based on the characteristics of different stages in the Swin Transformer. Specifically, a receptive field expansion block (RFEB) is designed to acquire contextual cues and extract detailed information. The RFEB is carefully designed to target the required receptive fields of different layers and further refine the features. MRFENet combined with RFEBs implements the retention of small object context cues and the acquisition of receptive fields for the adaptive detection tasks. Finally, a union loss function is designed to enhance the localization ability. Experiments on the MS COCO dataset demonstrate that the proposed MRFENet has a significant improvement against other state‐of‐the‐art methods, which further validates that MRFENet can effectively utilize small object information. |
first_indexed | 2024-03-13T07:58:03Z |
format | Article |
id | doaj.art-513154c93e3941e6bdec664af4813a83 |
institution | Directory Open Access Journal |
issn | 1751-9659 1751-9667 |
language | English |
last_indexed | 2024-03-13T07:58:03Z |
publishDate | 2023-06-01 |
publisher | Wiley |
record_format | Article |
series | IET Image Processing |
spelling | doaj.art-513154c93e3941e6bdec664af4813a832023-06-02T03:06:38ZengWileyIET Image Processing1751-96591751-96672023-06-011782385239810.1049/ipr2.12799Multilevel receptive field expansion network for small object detectionZhiwei Liu0Menghan Gan1Li Xiong2Xiaofeng Mao3Yue Que4School of Information Engineering East China Jiaotong University Nanchang ChinaSchool of Information Engineering East China Jiaotong University Nanchang ChinaSchool of Information Engineering East China Jiaotong University Nanchang ChinaSchool of Information Engineering East China Jiaotong University Nanchang ChinaSchool of Information Engineering East China Jiaotong University Nanchang ChinaAbstract Small object detection remains a bottleneck because there is little visual information about them, especially in the deep layers. To improve the detection performance of small objects, here, Swin Transformer is introduced as the model backbone network to extract rich features of small objects. Then, a multilevel receptive field expansion network (MRFENet) is proposed based on the characteristics of different stages in the Swin Transformer. Specifically, a receptive field expansion block (RFEB) is designed to acquire contextual cues and extract detailed information. The RFEB is carefully designed to target the required receptive fields of different layers and further refine the features. MRFENet combined with RFEBs implements the retention of small object context cues and the acquisition of receptive fields for the adaptive detection tasks. Finally, a union loss function is designed to enhance the localization ability. Experiments on the MS COCO dataset demonstrate that the proposed MRFENet has a significant improvement against other state‐of‐the‐art methods, which further validates that MRFENet can effectively utilize small object information.https://doi.org/10.1049/ipr2.12799neural netsobject detectionreceptive field expansionsmall object detectiontransformer |
spellingShingle | Zhiwei Liu Menghan Gan Li Xiong Xiaofeng Mao Yue Que Multilevel receptive field expansion network for small object detection IET Image Processing neural nets object detection receptive field expansion small object detection transformer |
title | Multilevel receptive field expansion network for small object detection |
title_full | Multilevel receptive field expansion network for small object detection |
title_fullStr | Multilevel receptive field expansion network for small object detection |
title_full_unstemmed | Multilevel receptive field expansion network for small object detection |
title_short | Multilevel receptive field expansion network for small object detection |
title_sort | multilevel receptive field expansion network for small object detection |
topic | neural nets object detection receptive field expansion small object detection transformer |
url | https://doi.org/10.1049/ipr2.12799 |
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