DLE-YOLO: An efficient object detection algorithm with dual-branch lightweight excitation network
As a computer vision task, object detection algorithms can be applied to various real-world scenarios. However, efficient algorithms often come with a large number of parameters and high computational complexity. To meet the demand for high-performance object detection algorithms on mobile devices a...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-03-01
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| Series: | Journal of Information and Intelligence |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949715924000751 |
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| author | Peitao Cheng Xuanjiao Lei Haoran Chen Xiumei Wang |
| author_facet | Peitao Cheng Xuanjiao Lei Haoran Chen Xiumei Wang |
| author_sort | Peitao Cheng |
| collection | DOAJ |
| description | As a computer vision task, object detection algorithms can be applied to various real-world scenarios. However, efficient algorithms often come with a large number of parameters and high computational complexity. To meet the demand for high-performance object detection algorithms on mobile devices and embedded devices with limited computational resources, we propose a new lightweight object detection algorithm called DLE-YOLO. Firstly, we design a novel backbone called dual-branch lightweight excitation network (DLEN) for feature extraction, which is mainly constructed by dual-branch lightweight excitation units (DLEU). DLEU is stacked with different numbers of dual-branch lightweight excitation blocks (DLEB), which can extract comprehensive features and integrate information between different channels of features. Secondly, in order to enhance the network to capture key feature information in the regions of interest, the attention model HS-coordinate attention (HS-CA) is introduced into the network. Thirdly, the localization loss utilizes SIoU loss to further optimize the accuracy of the bounding box. Our method achieves a mAP value of 46.0% on the MS-COCO dataset, which is a 2% mAP improvement compared to the baseline YOLOv5-m, while bringing a 19.3% reduction in parameter count and a 12.9% decrease in GFLOPs. Furthermore, our method outperforms some advanced lightweight object detection algorithms, validating the effectiveness of our approach. |
| format | Article |
| id | doaj-art-312a7dc00d5547d59eb0b2d86e9e530d |
| institution | Kabale University |
| issn | 2949-7159 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Information and Intelligence |
| spelling | doaj-art-312a7dc00d5547d59eb0b2d86e9e530d2025-08-20T03:29:34ZengKeAi Communications Co., Ltd.Journal of Information and Intelligence2949-71592025-03-01329110210.1016/j.jiixd.2024.08.002DLE-YOLO: An efficient object detection algorithm with dual-branch lightweight excitation networkPeitao Cheng0Xuanjiao Lei1Haoran Chen2Xiumei Wang3School of Mechano-Electronic Engineering, Xidian University, Xi'an 710071, ChinaSchool of Mechano-Electronic Engineering, Xidian University, Xi'an 710071, ChinaSchool of Mechano-Electronic Engineering, Xidian University, Xi'an 710071, ChinaSchool of Electronic Engineering, Xidian University, Xi'an 710071, China; Corresponding author.As a computer vision task, object detection algorithms can be applied to various real-world scenarios. However, efficient algorithms often come with a large number of parameters and high computational complexity. To meet the demand for high-performance object detection algorithms on mobile devices and embedded devices with limited computational resources, we propose a new lightweight object detection algorithm called DLE-YOLO. Firstly, we design a novel backbone called dual-branch lightweight excitation network (DLEN) for feature extraction, which is mainly constructed by dual-branch lightweight excitation units (DLEU). DLEU is stacked with different numbers of dual-branch lightweight excitation blocks (DLEB), which can extract comprehensive features and integrate information between different channels of features. Secondly, in order to enhance the network to capture key feature information in the regions of interest, the attention model HS-coordinate attention (HS-CA) is introduced into the network. Thirdly, the localization loss utilizes SIoU loss to further optimize the accuracy of the bounding box. Our method achieves a mAP value of 46.0% on the MS-COCO dataset, which is a 2% mAP improvement compared to the baseline YOLOv5-m, while bringing a 19.3% reduction in parameter count and a 12.9% decrease in GFLOPs. Furthermore, our method outperforms some advanced lightweight object detection algorithms, validating the effectiveness of our approach.http://www.sciencedirect.com/science/article/pii/S2949715924000751Object detectionLightweight networkAttention mechanismLoss function |
| spellingShingle | Peitao Cheng Xuanjiao Lei Haoran Chen Xiumei Wang DLE-YOLO: An efficient object detection algorithm with dual-branch lightweight excitation network Journal of Information and Intelligence Object detection Lightweight network Attention mechanism Loss function |
| title | DLE-YOLO: An efficient object detection algorithm with dual-branch lightweight excitation network |
| title_full | DLE-YOLO: An efficient object detection algorithm with dual-branch lightweight excitation network |
| title_fullStr | DLE-YOLO: An efficient object detection algorithm with dual-branch lightweight excitation network |
| title_full_unstemmed | DLE-YOLO: An efficient object detection algorithm with dual-branch lightweight excitation network |
| title_short | DLE-YOLO: An efficient object detection algorithm with dual-branch lightweight excitation network |
| title_sort | dle yolo an efficient object detection algorithm with dual branch lightweight excitation network |
| topic | Object detection Lightweight network Attention mechanism Loss function |
| url | http://www.sciencedirect.com/science/article/pii/S2949715924000751 |
| work_keys_str_mv | AT peitaocheng dleyoloanefficientobjectdetectionalgorithmwithdualbranchlightweightexcitationnetwork AT xuanjiaolei dleyoloanefficientobjectdetectionalgorithmwithdualbranchlightweightexcitationnetwork AT haoranchen dleyoloanefficientobjectdetectionalgorithmwithdualbranchlightweightexcitationnetwork AT xiumeiwang dleyoloanefficientobjectdetectionalgorithmwithdualbranchlightweightexcitationnetwork |