Journal of Guangxi Normal University(Natural Science Edition) ›› 2023, Vol. 41 ›› Issue (6): 22-32.doi: 10.16088/j.issn.1001-6600.2023040101

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Fritillaria ussuriensis Maxim Detection Model Based on Improved YOLOv5

ZHAO Wei, TIAN Shuai, ZHANG Qiang, WANG Yaoshen, WANG Sibo, SONG Jiang*   

  1. College of Engineering, Heilongjiang Bayi Agricultural Reclamation University, Daqing Heilongjiang 163319, China
  • Received:2023-04-01 Revised:2023-05-21 Published:2023-12-04

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Abstract: The existing Fritillaria ussuriensis Maxim topsoil stripping machine is not highly intelligent and may cause Fritillaria ussuriensis Maxim damage when harvesting Fritillaria ussuriensis Maxim, so it is necessary to develop an intelligent Fritillaria ussuriensis Maxim topsoil stripping machine, and the first step to realize intelligent topsoil stripping is to realize intelligent identification. Therefore, a Fritillaria ussuriensis Maxim detection model based on improved YOLOv5-YOLOv5-Swin-L is proposed. Swin Transformer is introduced to replace C3 in the backbone network to reduce the sequence length and computational complexity, thus simplifying the number of parameters in the model. At the same time, the activation function in the original network structure is replaced by ACON activation function, which can improve the accuracy and robustness of the model. Experimental results show that the improved YOLOv5-Swin-L can identify Fritillaria ussuriensis maxim with the highest accuracy of 96.39% and the highest recall rate of 95.76%, which is superior to other network models of YOLOv5 series.

Key words: Fritillaria ussuriensis Maxim, detection, YOLOv5, Swin transformer, ACON

CLC Number:  TP391.41;TP183;S225
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