Accurate detection and counting of apple flowers are essential for digital orchard monitoring and intelligent flower–fruit management. However, apple flowers in natural orchards are typically small, densely distributed, and subject to frequent occlusion, making conventional detection approaches unreliable. To address these challenges, we develop a D ensity- g uided D ual- s tage H igh- r esolution D etection framework (DG-DSHRD) combined with an enhanced YOLO-FRNet architecture. The proposed DG-DSHRD performs coarse detection to produce a density map and adaptively determines high-density regions for refined inference, reducing redundant computation while preserving structural continuity. YOLO-FRNet further improves small-object detection through an enhanced backbone, a lightweight multi-scale feature pyramid, and a quality-aware detection head, enabling more robust representation and localization under complex orchard backgrounds. Experiments conducted on a self-built distant-view apple flower detection dataset demonstrate that the combined framework effectively mitigates background interference, enhances feature fusion, and outperforms mainstream detectors and the slicing aided hyper inference(SAHI)-based static slicing. Compared with YOLOv11s using SAHI-based static slicing, the proposed YOLOv11s-FRNet + DG-DSHRD framework improves mAP₅₀ and F1 by 6.2 and 3.1 percentage points, respectively, reaching 66.6% and 66.3%, while increasing the inference speed from 2.85 FPS to 10.3 FPS, thereby enabling near real-time deployment. Overall, the DG-DSHRD + YOLO-FRNet framework provides an accurate, efficient, and deployable solution for high-density small-object detection in orchard environments, offering strong support for automated flower counting and intelligent flower–fruit regulation.
Huang et al. (Sun,) studied this question.