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

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.