Abstract: Fusarium head blight (FHB) is one of the most destructive diseases in global wheat production. In order to count the FHB-infected wheat ears under field conditions, this study proposed an algorithm for diseased wheat ear detection based on improved YOLOv5s (Tr-YOLOv5s). The Swin Transformer was used to replace the CSPDarknet backbone network to enhance the extraction of characteristic information of the population wheat ears of FHB in the field background. The convolutional block attention module (CBAM) attention mechanism was added to improve the detection effect of target wheat ears, subsequently improving the overall accuracy of the model. The original loss function complete intersection over union (CIoU) was replaced by Scylla intersection over union (SIoU) loss to accelerate the model convergence and decrease the loss value. The results showed that the mean average precision (mAP) of the Tr-YOLOv5s model reached 90.64%, making a 4.63% improvement compared to the original YOLOv5s model. The improved model could quickly detect and count wheat FHB ear in the field environment, which laid a foundation for the subsequent automatic disease identification and grading of wheat FHB under field conditions.