A path aggregation network with deformable convolution for visual object detection
One of the main challenges encountered in visual object detection is the multi-scale issue. Many approaches have been proposed to tackle this issue. In this article, we propose a novel neck that can perform effective fusion of multi-scale features for a single-stage object detector. This neck, named...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-08-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-3083.pdf |
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| author | Chengming Rao Zunhao Hu QiMing Zhao Min Shan Li Mao |
| author_facet | Chengming Rao Zunhao Hu QiMing Zhao Min Shan Li Mao |
| author_sort | Chengming Rao |
| collection | DOAJ |
| description | One of the main challenges encountered in visual object detection is the multi-scale issue. Many approaches have been proposed to tackle this issue. In this article, we propose a novel neck that can perform effective fusion of multi-scale features for a single-stage object detector. This neck, named the deformable convolution and path aggregation network (DePAN), is an integration of a path aggregation network with a deformable convolution block added to the feature fusion branch to improve the flexibility of feature point sampling. The deformable convolution block is implemented by repeated stacking of a deformable convolution cell. The DePAN neck can be plugged in and easily applied to various models for object detection. We apply the proposed neck to the baseline models of Yolov6-N and YOLOV6-T, and test the improved models on COCO2017 and PASCAL VOC2012 datasets, as well as a medical image dataset. The experimental results verify the effectiveness and applicability in real-world object detection. |
| format | Article |
| id | doaj-art-4d01f902ea1146aaab68ea05d0762494 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-4d01f902ea1146aaab68ea05d07624942025-08-20T15:05:19ZengPeerJ Inc.PeerJ Computer Science2376-59922025-08-0111e308310.7717/peerj-cs.3083A path aggregation network with deformable convolution for visual object detectionChengming Rao0Zunhao Hu1QiMing Zhao2Min Shan3Li Mao4College of Internet of Things Technology, Wuxi Institute of Technology, Wuxi, Jiangsu, ChinaSchool of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu, ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, ChinaOne of the main challenges encountered in visual object detection is the multi-scale issue. Many approaches have been proposed to tackle this issue. In this article, we propose a novel neck that can perform effective fusion of multi-scale features for a single-stage object detector. This neck, named the deformable convolution and path aggregation network (DePAN), is an integration of a path aggregation network with a deformable convolution block added to the feature fusion branch to improve the flexibility of feature point sampling. The deformable convolution block is implemented by repeated stacking of a deformable convolution cell. The DePAN neck can be plugged in and easily applied to various models for object detection. We apply the proposed neck to the baseline models of Yolov6-N and YOLOV6-T, and test the improved models on COCO2017 and PASCAL VOC2012 datasets, as well as a medical image dataset. The experimental results verify the effectiveness and applicability in real-world object detection.https://peerj.com/articles/cs-3083.pdfDePAN architectureFeature fusionObject detection |
| spellingShingle | Chengming Rao Zunhao Hu QiMing Zhao Min Shan Li Mao A path aggregation network with deformable convolution for visual object detection PeerJ Computer Science DePAN architecture Feature fusion Object detection |
| title | A path aggregation network with deformable convolution for visual object detection |
| title_full | A path aggregation network with deformable convolution for visual object detection |
| title_fullStr | A path aggregation network with deformable convolution for visual object detection |
| title_full_unstemmed | A path aggregation network with deformable convolution for visual object detection |
| title_short | A path aggregation network with deformable convolution for visual object detection |
| title_sort | path aggregation network with deformable convolution for visual object detection |
| topic | DePAN architecture Feature fusion Object detection |
| url | https://peerj.com/articles/cs-3083.pdf |
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