SF-YOLO11: A Real-Time Winter Jujube Detection Model Based on Lightweight Multi-Scale Fusion

To address the challenges of detecting winter jujubes in orchard environments—including dense small targets, variable illumination, complex backgrounds, and limited edge deployment capabilities—this paper proposes SF-YOLO11, a lightweight real-time detection model. First, a ternary channel importance metric is proposed that integrates gradient, variance, and task relevance to construct an adaptive pruning strategy. This strategy optimizes C3K2 into PrunedC3K2, achieving 31% model compression. Second, the LightSPPF module is designed through computational graph reconstruction. This module employs a three-stage architecture comprising depthwise separable convolution, adaptive pooling, and dynamic fusion, reducing computational cost by 40%. Finally, we transform multi-scale feature processing into sequence modeling and design the ISSFF module to capture scale dependencies through bidirectional LSTM and multi-head attention mechanisms, thereby improving small target detection accuracy. Experimental results demonstrate that SF-YOLO11 achieves an mAP@0.5 of 92.89%, representing a 3.68% improvement over the baseline. The model contains 1.6M parameters, requires 4.3 GFLOPs of computation, and operates at 128 FPS, achieving optimizations of 38.5%, 33.8%, and 14%, respectively. After INT8 quantization, the model size is reduced to only 1.7 MB, and the FPS increases to 215. In cross-fruit transfer experiments, the model achieves zero-shot mAP@0.5 values of 68.34% and 71.52% on grape and cherry tomato datasets, respectively. After fine-tuning, these values improve to 85.23% and 87.12%, respectively. Robustness tests validate the model's performance under extreme conditions, including strong illumination, low illumination, and occlusion.