BCP-YOLOv5: A High-Precision Object Detection Model for Peony Flower Recognition Based on YOLOv5

Peony flowers in Luoyang are renowned for their diverse varieties and substantial economic value. However, recognizing multiple peony varieties in natural field conditions remains challenging due to limited datasets and the shortcomings of existing detection models. High intra-class similarity among peony varieties, frequent occlusions, and imbalanced sample quality pose significant challenges to conventional approaches. To address these issues, we propose BCP-YOLOv5, an enhanced YOLOv5-based model designed for peony variety detection. The proposed model incorporates the Vision Transformer with Bi-Level Routing Attention (Biformer) to improve the detection accuracy of occluded targets. Inspired by Focal-EIoU, we redesign the loss function as Focal-CIoU to reduce the impact of low-quality samples and enhance bounding box localization. Additionally, Content-Aware Reassembly of Features (CARAFE) is employed to replace traditional upsampling, further improving performance. The experiments show that BCP-YOLOv5 improves precision by 3.4%, recall by 4.4%, and mAP@0.5 by 4.5% over baseline YOLOv5s. This work fills the gap in multi-variety peony detection and offers a practical, reproducible solution for intelligent agriculture.