Abstract: Aiming at the characteristics of dense sporangia distribution, cohesive stacking and complex background in the images collected by the greenhouse spore capture equipment, an improved YOLOv5s-based sporangia detection algorithm for cucumber downy mildew was proposed. Firstly, a Ghost convolution with CBAM (Convolutional Block Attention Module) attention mechanism was used to replace the CSP (Cross Stage Partial) module in the original network, which suppressed impurities in the background and ensured the generation of rich feature maps while reducing the number of model parameters and improving computational speed. Secondly, the connection method of the feature fusion network was modified, the original branch responsible for large object detection was deleted and a finergrained branch was added to strengthen the detection of small objects and dense, stacked objects. Finally, different weights were assigned to the loss values generated by different prediction heads, and the original NMS method was replaced by the DIOU_NMS nonmaximum value suppression method that considered the center point distance. The average accuracy and FPS of the improved YOLOv5s algorithm were 91.18% and 65.4 frames/s respectively, which were 4.88% and 7.1 frames/s higher than the original YOLOv5s algorithm. This study can provide data support for monitoring the occurrence and development of cucumber downy mildew, and is of great significance for ensuring the yield and quality of cucumbers.

Key words: deep learning, object detection, YOLOv5, cucumber downy mildew, sporangia

摘要： 针对温室孢子捕捉设备所采集图像中孢子囊分布密集、粘连堆叠和背景复杂的特点，提出一种基于改进YOLOv5s的黄瓜霜霉病孢子囊检测算法。首先，使用带CBAM(Convolutional Block Attention Module)注意力机制的Ghost卷积替代原始网络中的CSP(Cross Stage Partial)模块，抑制背景中的杂质，在保证产生丰富特征图的同时，降低模型的参数量，提升计算速度。其次，修改特征融合网络的连接方式，删除原来负责大物体检测的分支并加入一个更细粒度的分支，以加强对小目标和密集、堆叠目标的检测。最后，对不同预测头产生的损失值赋予不同的权重，并用考虑中心点距离的DIOU_NMS 非极大值抑制方法代替原来的NMS方法。改进后的YOLOv5s算法的平均准确率和FPS分别为91.18%和65.4帧/s，比原始的YOLOv5s算法高4.88%和7.1帧/s。该研究可为监测黄瓜霜霉病的发生和发展提供数据支撑，对于保障黄瓜的产量和质量具有重要意义。

关键词: 深度学习, 目标检测, YOLOv5, 黄瓜霜霉病, 孢子囊