Star-YOLO: A Lightweight Real-Time Wheat Grain Detection Model for Embedded Deployment
With the rapid advancement of precision agriculture, traditional object detection algorithms struggle with limited efficiency and accuracy in wheat grain detection and counting, while the need for real-time deployment of deep learning models on embedded devices becomes increasingly critical. To this...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11087564/ |
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| author | Zhihang Qu Xiao Liang Sicheng Liang Xiumei Guo |
| author_facet | Zhihang Qu Xiao Liang Sicheng Liang Xiumei Guo |
| author_sort | Zhihang Qu |
| collection | DOAJ |
| description | With the rapid advancement of precision agriculture, traditional object detection algorithms struggle with limited efficiency and accuracy in wheat grain detection and counting, while the need for real-time deployment of deep learning models on embedded devices becomes increasingly critical. To this end, this paper introduces Star-YOLO, a lightweight wheat grain detection model built upon YOLOv11n. The model employs StarNet to refine the C3k2 structure, reducing computational complexity without compromising detection accuracy, and integrates the MBConv module into the detection head to boost feature extraction while further minimizing computational load. A Shape-NWD loss function is designed, incorporating shape and scale information of target bounding boxes to refine regression, tackling the challenge of distinguishing overlapping wheat grains. Experimental results indicate that Star-YOLO attains a mean Average Precision (mAP) of 97.3% in wheat grain detection, outperforming models like YOLOv5n and RT-DETR in both accuracy and efficiency, with a reduced parameter count. Tests on embedded devices reveal a 36.8% performance enhancement over the baseline model, fulfilling the demands of real-time detection. The Star-YOLO model markedly enhances both the accuracy and efficiency of wheat grain detection while underscoring the potential of deep learning in agricultural image analysis and smart wheat industry advancements. |
| format | Article |
| id | doaj-art-18340fdbcbbf4baa94999c87d4f36f41 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-18340fdbcbbf4baa94999c87d4f36f412025-08-20T03:31:30ZengIEEEIEEE Access2169-35362025-01-011313014713015810.1109/ACCESS.2025.359137411087564Star-YOLO: A Lightweight Real-Time Wheat Grain Detection Model for Embedded DeploymentZhihang Qu0https://orcid.org/0009-0004-0701-586XXiao Liang1Sicheng Liang2Xiumei Guo3https://orcid.org/0000-0002-4953-5554School of Information Science and Engineering, Shandong Agricultural University, Taian, ChinaSchool of Information Science and Engineering, Shandong Agricultural University, Taian, ChinaSchool of Information Science and Engineering, Shandong Agricultural University, Taian, ChinaSchool of Information Science and Engineering, Shandong Agricultural University, Taian, ChinaWith the rapid advancement of precision agriculture, traditional object detection algorithms struggle with limited efficiency and accuracy in wheat grain detection and counting, while the need for real-time deployment of deep learning models on embedded devices becomes increasingly critical. To this end, this paper introduces Star-YOLO, a lightweight wheat grain detection model built upon YOLOv11n. The model employs StarNet to refine the C3k2 structure, reducing computational complexity without compromising detection accuracy, and integrates the MBConv module into the detection head to boost feature extraction while further minimizing computational load. A Shape-NWD loss function is designed, incorporating shape and scale information of target bounding boxes to refine regression, tackling the challenge of distinguishing overlapping wheat grains. Experimental results indicate that Star-YOLO attains a mean Average Precision (mAP) of 97.3% in wheat grain detection, outperforming models like YOLOv5n and RT-DETR in both accuracy and efficiency, with a reduced parameter count. Tests on embedded devices reveal a 36.8% performance enhancement over the baseline model, fulfilling the demands of real-time detection. The Star-YOLO model markedly enhances both the accuracy and efficiency of wheat grain detection while underscoring the potential of deep learning in agricultural image analysis and smart wheat industry advancements.https://ieeexplore.ieee.org/document/11087564/Wheat grainsYOLOv11lightweight architectureshape-NWDreal-time detection |
| spellingShingle | Zhihang Qu Xiao Liang Sicheng Liang Xiumei Guo Star-YOLO: A Lightweight Real-Time Wheat Grain Detection Model for Embedded Deployment IEEE Access Wheat grains YOLOv11 lightweight architecture shape-NWD real-time detection |
| title | Star-YOLO: A Lightweight Real-Time Wheat Grain Detection Model for Embedded Deployment |
| title_full | Star-YOLO: A Lightweight Real-Time Wheat Grain Detection Model for Embedded Deployment |
| title_fullStr | Star-YOLO: A Lightweight Real-Time Wheat Grain Detection Model for Embedded Deployment |
| title_full_unstemmed | Star-YOLO: A Lightweight Real-Time Wheat Grain Detection Model for Embedded Deployment |
| title_short | Star-YOLO: A Lightweight Real-Time Wheat Grain Detection Model for Embedded Deployment |
| title_sort | star yolo a lightweight real time wheat grain detection model for embedded deployment |
| topic | Wheat grains YOLOv11 lightweight architecture shape-NWD real-time detection |
| url | https://ieeexplore.ieee.org/document/11087564/ |
| work_keys_str_mv | AT zhihangqu staryoloalightweightrealtimewheatgraindetectionmodelforembeddeddeployment AT xiaoliang staryoloalightweightrealtimewheatgraindetectionmodelforembeddeddeployment AT sichengliang staryoloalightweightrealtimewheatgraindetectionmodelforembeddeddeployment AT xiumeiguo staryoloalightweightrealtimewheatgraindetectionmodelforembeddeddeployment |