A Robust YOLOv5 Model with SE Attention and BIFPN for Jishan Jujube Detection in Complex Agricultural Environments
This study presents an improved detection model based on the YOLOv5 (You Only Look Once version 5) framework to enhance the accuracy of Jishan jujube detection in complex natural environments, particularly with varying degrees of occlusion and dense foliage. To improve detection performance, we inte...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/6/665 |
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| author | Hao Chen Lijun Su Yiren Tian Yixin Chai Gang Hu Weiyi Mu |
| author_facet | Hao Chen Lijun Su Yiren Tian Yixin Chai Gang Hu Weiyi Mu |
| author_sort | Hao Chen |
| collection | DOAJ |
| description | This study presents an improved detection model based on the YOLOv5 (You Only Look Once version 5) framework to enhance the accuracy of Jishan jujube detection in complex natural environments, particularly with varying degrees of occlusion and dense foliage. To improve detection performance, we integrate an SE (squeeze-and-excitation) attention module into the backbone network to enhance the model’s ability to focus on target objects while suppressing background noise. Additionally, the original neck network is replaced with a BIFPN (bi-directional feature pyramid network) structure, enabling efficient multiscale feature fusion and improving the extraction of critical features, especially for small and occluded fruits. The experimental results demonstrate that the improved YOLOv5 model achieves a mean average precision (mAP) of 96.5%, outperforming the YOLOv3, YOLOv4, YOLOv5, and SSD (Single-Shot Multibox Detector) models by 7.4%, 9.9%, 2.5%, and 0.8%, respectively. Furthermore, the proposed model improves precision (95.8%) and F1 score (92.4%), reducing false positives and achieving a better balance between precision and recall. These results highlight the model’s effectiveness in addressing missed detections of small and occluded fruits while maintaining higher confidence in predictions. |
| format | Article |
| id | doaj-art-14127592e62e49dfa2546166bc33f5fd |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-14127592e62e49dfa2546166bc33f5fd2025-08-20T03:40:42ZengMDPI AGAgriculture2077-04722025-03-0115666510.3390/agriculture15060665A Robust YOLOv5 Model with SE Attention and BIFPN for Jishan Jujube Detection in Complex Agricultural EnvironmentsHao Chen0Lijun Su1Yiren Tian2Yixin Chai3Gang Hu4Weiyi Mu5School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Science, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Science, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Science, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Science, Xi’an University of Technology, Xi’an 710048, ChinaThis study presents an improved detection model based on the YOLOv5 (You Only Look Once version 5) framework to enhance the accuracy of Jishan jujube detection in complex natural environments, particularly with varying degrees of occlusion and dense foliage. To improve detection performance, we integrate an SE (squeeze-and-excitation) attention module into the backbone network to enhance the model’s ability to focus on target objects while suppressing background noise. Additionally, the original neck network is replaced with a BIFPN (bi-directional feature pyramid network) structure, enabling efficient multiscale feature fusion and improving the extraction of critical features, especially for small and occluded fruits. The experimental results demonstrate that the improved YOLOv5 model achieves a mean average precision (mAP) of 96.5%, outperforming the YOLOv3, YOLOv4, YOLOv5, and SSD (Single-Shot Multibox Detector) models by 7.4%, 9.9%, 2.5%, and 0.8%, respectively. Furthermore, the proposed model improves precision (95.8%) and F1 score (92.4%), reducing false positives and achieving a better balance between precision and recall. These results highlight the model’s effectiveness in addressing missed detections of small and occluded fruits while maintaining higher confidence in predictions.https://www.mdpi.com/2077-0472/15/6/665YOLOv5SE attention mechanismBIFPNJishan jujube detectionprecision agriculture |
| spellingShingle | Hao Chen Lijun Su Yiren Tian Yixin Chai Gang Hu Weiyi Mu A Robust YOLOv5 Model with SE Attention and BIFPN for Jishan Jujube Detection in Complex Agricultural Environments Agriculture YOLOv5 SE attention mechanism BIFPN Jishan jujube detection precision agriculture |
| title | A Robust YOLOv5 Model with SE Attention and BIFPN for Jishan Jujube Detection in Complex Agricultural Environments |
| title_full | A Robust YOLOv5 Model with SE Attention and BIFPN for Jishan Jujube Detection in Complex Agricultural Environments |
| title_fullStr | A Robust YOLOv5 Model with SE Attention and BIFPN for Jishan Jujube Detection in Complex Agricultural Environments |
| title_full_unstemmed | A Robust YOLOv5 Model with SE Attention and BIFPN for Jishan Jujube Detection in Complex Agricultural Environments |
| title_short | A Robust YOLOv5 Model with SE Attention and BIFPN for Jishan Jujube Detection in Complex Agricultural Environments |
| title_sort | robust yolov5 model with se attention and bifpn for jishan jujube detection in complex agricultural environments |
| topic | YOLOv5 SE attention mechanism BIFPN Jishan jujube detection precision agriculture |
| url | https://www.mdpi.com/2077-0472/15/6/665 |
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