%0 Journal Article %A YANG Qisheng %A LI Wenkuan %A YANG Xiaofeng %A YUE Linxi %A LI Haifang %T Improved YOLOv5 Method for Detecting Growth Status of Apple Flowers %D 2022 %R 10.3778/j.issn.1002-8331.2107-0523 %J Computer Engineering and Applications %P 237-246 %V 58 %N 4 %X Aiming at the problem that it is difficult for the existing target detection algorithm to detect the growth status of apple flowers in the complex environment of the orchard, an improved YOLOv5 method for detecting the growth status of apple flowers is proposed. The growth status of the flowers during the flowering period of the tree is tested. This method firstly improves the cross-stage local network module, adjusts the number of modules, and designs the backbone network with the collaborative attention module to improve model detection performance and reduce parameters. Secondly, this method combines the new detection scale and the split-based convolution operation to design a feature fusion network to improve the network feature fusion ability, and finally chooses CIoU as the loss function of the frame regression to achieve high-precision positioning. The improved algorithm is compared with the original YOLOv5 algorithm on a self-built data set. The experimental results show that the mAP of the algorithm in this paper reaches 0.922, which is 5.4 percentage points higher than that of YOLOv5. Compared with other mainstream algorithms, the detection accuracy is greatly improved, which proves the effectiveness of the algorithm. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2107-0523