A rice leaf disease detection model based on ATSS is proposed to address the shortcomings of traditional rice disease diagnosis methods that rely on manual labor and are prone to misjudgment.Firstly,the images of white leaf blight,brown spot and leaf blast were collected to construct rice leaf disease image dataset,then,based on the original ATSS model,the Neck part of the network uses an FPN-CARAFE module instead of FPN module to reduce information loss during the up-sampling process;FPN-CARAFE module is used to replace the characteristic pyramid network FPN in the network Neck to reduce the information loss in the up-sampling process based on the original ATSS model.Meanwhile,to improve the detection effect of the model,the loss function of the regression branch adopts the CIoU instead of GIoU.The mean average precision of the improved ATSS model can reach 74.0%,which is 3.5%higher than that of the original ATSS model.Compared with models Retinanet,Faster R-CNN,Cascade R-CNN,FCOS and TOOD,the improved ATSS model has the highest detection accuracy and the highest weight in detection accuracy and speed.The experimental results indicate the improved model can effectively detect rice leaf diseases.
关键词
改进ATSS模型/FPN-CARAFE/CIoU损失函数/水稻叶片病害
Key words
Improved ATSS model/FPN-CARAFE module/CIoU loss function/rice leaf disease
维普
万方数据