Aiming at the problems of large difference in accuracy and low detection accuracy of various types of apple leaf diseases,an improved YOLOv5 detection algorithm for accurate identification of apple leaf diseases(YOLOV5-CSEP)was proposed.Firstly,C3Ghost module was introduced to replace the C3 module of YOLOv5 backbone network to reduce the number of parameters and calculation amount of the model.Secondly,the hybrid attention module C-SAM was added to the backbone network to improve the feature extraction capability of the backbone network,and the CA attention module was added to the neck network to suppress the interference of complex background information.Finally,an enhanced path aggregation network(E-PANet)was introduced to fully integrate multi-scale features and improve the accuracy and robustness of the network to detect various types of apple leaf diseases.Experiments showed that all performance indexes of the improved algorithm were improved,the accuracy rate reached 93.2％,and the average accuracy(mAP@0.5)reached 87.9％.Compared with the original YOLOv5 algorithm,it was improved by 3.4％and 1.7％respectively,and the calculation amount was reduced by 11％.
关键词
苹果叶片/病害检测/注意力机制/增强路径聚合网络/YOLOv5
Key words
apple tree leaf/disease detection/attention mechanism/enhanced path aggregation network/YOLOv5
维普
万方数据