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

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.