Research on image recognition technology based on improved YOLOv5

In order to improve the performance of image recognition, the traditional YOLOv5s network was improved and applied to wheat pest recognition. Based on the analysis of YOLOv5s, the activation function, attention module, feature fusion network and loss function are improved. By comparing YOLOv5s with other image recognition technologies, it is pointed out that the improved YOLOv5s network has good performance in three aspects: accuracy, recall rate and average accuracy. Compared with other target recognition algorithms, the improved YOLOv5s network can be better applied to target recognition in complex scenes containing small targets. It has certain practical value for improving the efficiency of wheat pest detection and realizing the transformation from traditional agriculture to modern agriculture.