关键词: 深度学习, YOLOv5s, 茶叶病害, 注意力机制, 目标检测

Abstract: In natural scenes, tea diseases have different shapes and small targets, and traditional convolutional neural networks are not suitable for disease detection under complex background. Therefore, we proposed an improved tea disease target detection algorithm, YOLOv5sECAASFF. This algorithm introduces ECA channel attention module to enhance the global context information in the channel dimension, and uses adaptive spatial feature fusion (ASFF) technology to improve the multiscale feature fusion of tea diseases and improve the background antijamming ability of the model. At the same time, the GIoU loss function is used as the bounding box loss function to further improve the detection accuracy of the regression target. Compared with the original YOLOv5s model, the average accuracy of the improved YOlOv5SECAASFF model in the identification of tea white star disease, tea wheel spot disease, tea anthracnose disease and tea algal spot disease was increased by 5%, 4%, 3% and 2%, respectively, and the average accuracy was 92.1%. In addition, the image detection speed of this model is 64 f/s, and the comprehensive performance is better than that of YOLOv4, SSD and Faster RCNN models. Therefore, the model provides a reference for the detection of different kinds of tea diseases in the natural growing environment, and provides important technical support for early prediction.

Key words: deep learning, YOLOv5s, tea diseases, attention mechanism, target detection