Abstract: To unify identification standards and improve the detection accuracy and real‑time performance of mature Hemerocallis citrina Baroni picking，an improved GCS‑BI YOLOv5 image detection algorithm was proposed.Firstly，the Ghost lightweight neural networks were utilized to streamline the model structure and save computational resources.Secondly，in order to pay attention to the image channel information and position information simultaneously，efficient attention mechanisms，namely convolutional block attention module（CBAM）and squeeze‑and‑excitation（SE），were cross‑introduced to improve the image feature perception ability and model convergence speed.Then，a weighted bi‑directional feature pyramid network（BI FPN）was used to fuse the multi‑scale image information and improve the comprehensive detection performance of the model for different targets.The experimental results showed that compared with the original algorithm，the lightweight metrics such as the model volume，network layers，number of parameters，and floating‑point operation of the improved algorithm were reduced by 62.89%，33.12%，63.01%，68.39%，respectively.The performance metrics such as detection accuracy and recall rate were improved by 7.77，6.28 percentage points，respectively.Real‑time detection performance was improved by 33.81 f/s.It can be seen that the improved algorithm has better comprehensive performance and can meet the requirements of Hemerocallis citrina Baroni maturity detection.

摘要： 为统一辨识标准，提高成熟黄花采摘的检测精度和实时性能，提出一种改进的GCS-BI YOLOv5图像检测算法。首先，利用轻量化神经网络（Ghost Net）精简模型结构，节省计算资源；其次，为兼顾图像通道信息、位置信息，交叉引入高效注意力机制（CBAM和SE），提升图像特征感知能力和模型收敛速度；然后，采用加权双向特征金字塔网络（BI FPN），融合多尺度图像信息，提升模型对不同目标的综合检测性能。结果表明，与原始算法YOLOv5相比，所提算法在模型体积、网络层数、参数量、浮点运算量等轻量化指标方面分别下降62.89%、33.12%、63.01%、68.39%；在精确度、召回率等性能指标方面分别提升7.77、6.28百分点；实时性能提升了33.81 f/s。可见，改进算法的综合性能较优，能够满足黄花成熟度检测的要求。

关键词: 黄花, 轻量化神经网络, 注意力机制, 跨通道特征融合, 深度学习, YOLOv5