Depth recognition of branch obstacles of apple picking robot based on improved YOLOv4
In computer vision,it was difficult to train and recognize objects with unclear features,and improve the detection in many fields.In order to identify the branches with less obvious characteristics,mainly the branches that covered the apple position when the manipulator picked the apple,a method to obtain the branch semantic skeleton and identify the branch position box by combining se-mantic segmentation and YOLOv4 algorithm was proposed.Before using the data set for training,the method of semantic segmentation to divide the rectangular envelope of branches,eliminate the small branches and branches that affected the effect of branch recogni-tion,and then label the data set with labelimg and labelme tools was used;Three layers of maximum pooling layer were added to the trained network model,and the CIOU of YOLOv4 was improved in terms of regression loss.A confidence correlation function BIOU was proposed to improve the prediction accuracy according to the complex characteristics and suitable range.The final experiment showed that the F1 and AP of the tree branches with occluded apple positions trained by the improved YOLOv4 network model were 20.00 precentage and 23.36 precentage higher than those of all the trees trained by the original network.
branch recognitionYOLOv4semantic segmentationdataset trainingBIOU frame regression loss function
NETL
NSTL
万方数据
