PCPE-YOLO with a lightweight dynamically reconfigurable backbone for small object detection
Abstract In the domain of object detection, small object detection remains a pressing challenge, as existing approaches often suffer from limited accuracy, high model complexity, and difficulty meeting lightweight deployment requirements. In this paper, we propose PCPE-YOLO, a novel object detection...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-15975-w |
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| author | Weijia Chen Jiaming Liu Tong Liu Yaoming Zhuang |
| author_facet | Weijia Chen Jiaming Liu Tong Liu Yaoming Zhuang |
| author_sort | Weijia Chen |
| collection | DOAJ |
| description | Abstract In the domain of object detection, small object detection remains a pressing challenge, as existing approaches often suffer from limited accuracy, high model complexity, and difficulty meeting lightweight deployment requirements. In this paper, we propose PCPE-YOLO, a novel object detection algorithm, specifically designed to address these difficulties. First, we put forward a dynamically reconfigurable C2f_PIG module. This module uses a parameter-aware mechanism to adapt its bottleneck structures to different network depths and widths, reducing parameters while maintaining performance. Next, we introduce a Context Anchor Attention mechanism that boosts the model’s focus on the contexts of small objects, thereby improving detection accuracy. In addition, we add a small object detection layer to enhance the model’s localization capability for small objects. Finally, we integrate an Efficient Up-Convolution Block to sharpen decoder feature maps, enhancing small object recall with minimal computational overhead. Experiments on VisDrone2019, KITTI, and NWPU VHR-10 datasets show that PCPE-YOLO significantly outperforms both the baseline and other state-of-the-art methods in precision, recall, mean average precision, and parameters, achieving the best precision among all compared approaches. On VisDrone2019 in particular, it achieves improvements of 3.8% in precision, 5.6% in recall, 6.2% in mAP50, and 5% in F1 score, effectively combining lightweight design with high small object detection performance and providing a more efficient and reliable solution for small object detection in real-world applications. |
| format | Article |
| id | doaj-art-0610a787df0b42e6911260268d03d09f |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0610a787df0b42e6911260268d03d09f2025-08-20T03:07:26ZengNature PortfolioScientific Reports2045-23222025-08-0115111510.1038/s41598-025-15975-wPCPE-YOLO with a lightweight dynamically reconfigurable backbone for small object detectionWeijia Chen0Jiaming Liu1Tong Liu2Yaoming Zhuang3Faculty of Business Administration, Northeastern UniversityFaculty of Robot Science and Engineering, Northeastern UniversityFaculty of Business Administration, Northeastern UniversityFaculty of Robot Science and Engineering, Northeastern UniversityAbstract In the domain of object detection, small object detection remains a pressing challenge, as existing approaches often suffer from limited accuracy, high model complexity, and difficulty meeting lightweight deployment requirements. In this paper, we propose PCPE-YOLO, a novel object detection algorithm, specifically designed to address these difficulties. First, we put forward a dynamically reconfigurable C2f_PIG module. This module uses a parameter-aware mechanism to adapt its bottleneck structures to different network depths and widths, reducing parameters while maintaining performance. Next, we introduce a Context Anchor Attention mechanism that boosts the model’s focus on the contexts of small objects, thereby improving detection accuracy. In addition, we add a small object detection layer to enhance the model’s localization capability for small objects. Finally, we integrate an Efficient Up-Convolution Block to sharpen decoder feature maps, enhancing small object recall with minimal computational overhead. Experiments on VisDrone2019, KITTI, and NWPU VHR-10 datasets show that PCPE-YOLO significantly outperforms both the baseline and other state-of-the-art methods in precision, recall, mean average precision, and parameters, achieving the best precision among all compared approaches. On VisDrone2019 in particular, it achieves improvements of 3.8% in precision, 5.6% in recall, 6.2% in mAP50, and 5% in F1 score, effectively combining lightweight design with high small object detection performance and providing a more efficient and reliable solution for small object detection in real-world applications.https://doi.org/10.1038/s41598-025-15975-wYOLOv8Object detectionSmall objectLightweight |
| spellingShingle | Weijia Chen Jiaming Liu Tong Liu Yaoming Zhuang PCPE-YOLO with a lightweight dynamically reconfigurable backbone for small object detection Scientific Reports YOLOv8 Object detection Small object Lightweight |
| title | PCPE-YOLO with a lightweight dynamically reconfigurable backbone for small object detection |
| title_full | PCPE-YOLO with a lightweight dynamically reconfigurable backbone for small object detection |
| title_fullStr | PCPE-YOLO with a lightweight dynamically reconfigurable backbone for small object detection |
| title_full_unstemmed | PCPE-YOLO with a lightweight dynamically reconfigurable backbone for small object detection |
| title_short | PCPE-YOLO with a lightweight dynamically reconfigurable backbone for small object detection |
| title_sort | pcpe yolo with a lightweight dynamically reconfigurable backbone for small object detection |
| topic | YOLOv8 Object detection Small object Lightweight |
| url | https://doi.org/10.1038/s41598-025-15975-w |
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