Identification method of pod pepper fruits based on improved YOLOv7 model
The accurate identification of pod pepper fruits is the crucial step to realize intelligent picking.Aiming at the problem of low recognition accuracy caused by complex growing environments,different fruit sizes,and occlusion and overlap-ping,a fruit recognition method based on improved YOLOv7 was proposed.Using YOLOv7 as the basic model,an AM_F module with residual structure was designed and integrated into the backbone network of YOLOv7.The SAM_F and SE_ECA modules were obtained by improving the structure of the spatial and channel attention mechanisms.They were integrated into the backbone network and the neck network respectively,and the structure was simplified.At the same time,the SPP in the SPP_CSP struc-ture was replaced by SPPF to simplify the calculation of parameters,and finally an improved YOLOv7 model,YOLOv7-F,was obtained.The recognition effect of YOLOv7-F model was verified and analyzed by comparison tests.The results indicated that the average recognition accuracy of YOLOv7-F model was 80.07%.Compared with the YOLOv7 model,the recognition time of the YOLOv7-F model was accelerated by 23.4 ms,the average accuracy was increased by 1.06 percentage points,and the model size was reduced by 77.94 MB.The YOLOv7-F model can realize the synchronous improvement of the recognition accuracy and recog-nition speed of pod pepper fruits,and provide technical support for the intelligent picking of pod pepper fruits.
万方数据
维普
