Dangshan Pear Target Detection Based on Mask R-CNN
Target recognition is of vital importance to picking automation in the fruit industry,but the tra-ditional detection algorithm is not sufficient to recognize pears in the natural environment.Based on the Mask R-CNN(mask region-convolutional neural network)model,and combined with the sample database of Dangshan pear images,image features were extracted by the feature pyramid network(FPN),the fea-ture map was processed by RPN(region proposal network),and then the effectiveness of Dangshan pear target detection was analyzed.Results showed that the accuracy of Mask R-CNN model for Dangshan pear target detection was 95.54％,the recall was 92.79％,and the false rate was 4.45％.This Mask R-CNN model could detect the complete outlines of Dangshan pears accurately in situations where fruits were ob-structed by branches and leaves,or not obstructed by branches and leaves,or overlapped,etc.It provides technical support for picking robots to detect pear targets.
万方数据
维普
