Research on Wheat Ears Detection Method Based on Improved YOLOv5

Abstract

Wheat ears detection and counting play a crucial role in wheat yield prediction and breeding. In this paper, a deep neural network wheat ears detection method Wheat-YOLOv5 based on improved YOLOv5 is proposed for the problem of low accuracy of traditional wheat ears detection methods. Fusion of the ECANet attention module with the Backbone part of the YOLOv5s network to improve network feature extraction. Using SPConv convolution to replace the original ordinary convolution in the neck layer to improve the model’s ability to cope with complex scenes of wheat ears. Using $$\alpha $$ EIoU Loss instead of GIoU Loss as the target bounding box regression loss function to improve the accuracy of wheat ears localization. The detection average accuracy AP value of the improved algorithm reaches 94.30% and F1 value reaches 91.50%, which has certain recognition accuracy and robustness and can effectively improve the detection of wheat ears in actual agricultural scenes.