YOLO-MSNet: Real-Time Detection Algorithm for Pomegranate Fruit Improved by YOLOv11n

Liang Xu, Bing Li (), Xue Fu, Zhe Lu, Zelong Li, Bai Jiang and Siye Jia
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Liang Xu: College of linteligent Systems Science and Engineering, Harbin Engineering University, Nantong Street, Harbin 150001, China
Bing Li: College of linteligent Systems Science and Engineering, Harbin Engineering University, Nantong Street, Harbin 150001, China
Xue Fu: College of linteligent Systems Science and Engineering, Harbin Engineering University, Nantong Street, Harbin 150001, China
Zhe Lu: College of linteligent Systems Science and Engineering, Harbin Engineering University, Nantong Street, Harbin 150001, China
Zelong Li: College of linteligent Systems Science and Engineering, Harbin Engineering University, Nantong Street, Harbin 150001, China
Bai Jiang: College of linteligent Systems Science and Engineering, Harbin Engineering University, Nantong Street, Harbin 150001, China
Siye Jia: College of linteligent Systems Science and Engineering, Harbin Engineering University, Nantong Street, Harbin 150001, China

Agriculture, 2025, vol. 15, issue 10, 1-20

Abstract: In complex orchard environments, rapidly and accurately identifying pomegranate fruits at various growth stages remains a significant challenge. Therefore, we propose YOLO-MSNet, a lightweight and enhanced pomegranate fruit detection model developed using YOLOv11. Firstly, the C3k2_UIB module is elegantly designed by integrating the Universal Inverted Bottleneck (UIB) structure into the model, while convolutional modules within the model are seamlessly replaced by AKConv units, thereby markedly reducing the overall complexity of the model. Subsequently, a novel parallel cascaded attention module called SSAM is designed as a way to improve the model’s ability to clearly see small details of the fruit against the background of a complex orchard. Additionally, a Dynamic Adaptive Bidirectional Feature Pyramid Network (DA-BiFPN) that employs adaptive sampling strategies to optimize multi-scale feature fusion is designed. The C3k2_UIB module complements this by reinforcing feature interactions and information aggregation across various scales, thereby enhancing the model’s perception of multi-scale objects. Furthermore, integrating VFLoss and ShapeIOU further refines the model’s ability to distinguish between overlapping and differently sized targets. Finally, comparative evaluations conducted on a publicly available pomegranate fruit dataset against state-of-the-art models demonstrate that YOLO-MSNet achieves a 1.7% increase in mAP50, a 21.5% reduction in parameter count, and a 21.8% decrease in model size. Further comparisons with mainstream YOLO models confirm that YOLO-MSNet has a superior detection accuracy despite being significantly lighter, making it especially suitable for deployment in resource-constrained edge devices, effectively addressing real-world requirements for fruit detection in complex orchard environments.

Keywords: lightweight; SSAM; C3k2_UIB; target detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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