基于改进YOLO 11的海鲜菇生长阶段精细化分类方法

Fine-grained Classification of Growth Stages of Seafood Mushroom Based on Improved YOLO 11

摘要:

生长阶段精细化分类是实现海鲜菇菇房环境智慧、精准调控的前提。然而，由于海鲜菇调控所需生长阶段划分较为精细，且相邻阶段的表型特征高度相似，使其生长阶段的精细化分类难度较大。针对此问题本研究提出一种改进YOLO 11生长阶段精细化分类方法。首先，在YOLO 11主干网络中融合全局注意力机制（Global attention mechanism,GAM），通过增强通道注意力和空间注意力，更有效地提取海鲜菇的关键特征；其次，将激活函数由SiLU更改为Mish，有效增强了网络的非线性表达能力；最后将原始卷积优化为幻影卷积，在保持高精度目标检测的同时，简化模型结构并优化了计算效率。本文所改进模型的识别准确率为96.97%，召回率为96.73%，平均精度均值为96.58%，精确率为96.81%，并且模型的推理时间和模型参数量分别缩减了4.28%和21.69%，优于RF-SVM、ResNet50、YOLO v8和YOLO 11。这些结果表明，本文所提出的改进方法具备更优的综合性能，能够有效地应用于海鲜菇生长阶段精细化分类。

Abstract:

Fine-grained classification of growth stages is a prerequisite for achieving intelligent and precise environmental control in seafood mushroom cultivation. However,due to the subtle phenotypic differences between adjacent growth stages and the high granularity of stage division required for regulation,accurate classification remains challenging. An enhanced YOLO 11-based method for fine-grained growth stage classification was proposed. Firstly, a global attention mechanism(GAM) was integrated into the YOLO 11 backbone network to enhance channel and spatial attention, thereby improving the extraction of discriminative features. Secondly,the activation function was replaced with Mish to strengthen the network’s nonlinear representation capability.Finally, the original convolution was optimized to GhostConv,simplifying the model architecture while maintaining high detection accuracy and computational efficiency.Experimental results demonstrated that the improved algorithm achieved a recognition accuracy of 96.97%, a recall rate of 96.73%, a mean average precision (mAP) of 96.58%, and a precision of 96.81%.Furthermore, the inference time and model parameters were reduced by 4.28% and 21.69%, respectively, outperforming that of RF-SVM,ResNet50, YOLO v8,and the original YOLO 11. These results indicated that the proposed method exhibited superior comprehensive performance and can be effectively applied to fine-grained classification of seafood mushroom growth stages,providing a robust foundation for intelligent environmental regulation in mushroom cultivation.