关键词: 蝴蝶兰花朵, 深度学习, 目标检测, YOLOv5, 聚类算法, 注意力机制

Abstract:  In order to accurately predict the yield of phalaenopsis and scientifically manage the production of phalaenopsis, this study identified and detected the flowers and buds of phalaenopsis plants in the big seedling stage and counted the number of flowers and buds. Due to the small target volume of phalaenopsis bud,, a phalaenopsis flower and bud recognition method based on improved YOLOv5 was proposed. Firstly, the structure of the neck network was modified in feature pyramid network and Path Aggregation Network. A 160×160 feature map was introduced to improve the detection effect of small targets. Secondly, the Kmeans++ clustering algorithm was used to generate a more appropriate anchor box for the training set, and the training method of loading pretraining weights and freezing the backbone network was adopted to make the model easier to learn and improve the convergence speed and generalization ability of the network model. Finally, a lightweight attention mechanism was added to the neck network to strengthen the attention to the target and reduce the background interference, to improve the feature extraction ability of the model. The experimental results showed that the AP of the algorithm for buds was 89.54%, which was 9.83% higher than that before the improvement, and the mAP of buds and flowers was 91.81%, which was 5.56% higher than that before the improvement. So, the algorithm has excellent detection accuracy and effectively improves the detection ability of small targets.

Key words: phalaenopsis flower, deep learning, target detection, YOLOv5, clustering algorithm, attention mechanism