LAUSR

Abstract Regarding the identification and location of bayberries in the natural environment, the work applied the dilated convolution to the res4b module of Mask RCNN backbone network—ResNet. The method was used to realize the accurate identification and segmentation of waxberry. First, we pre-trained the d -MRCNN network transformed by the dilated convolution with the COCO dataset. Then, through the migration learning method, the representative waxberry dataset was used to train the network for the identification and segmentation of waxberry. Finally, based on the same verification sample set, the work compared the Ostu and K-Means with the deep learning segmentation networks U-net and FCN. The result showed that the algorithm in this work was optimal, with the average detection accuracy and recall rate reaching 97 % and 91 %, respectively. It has high generalization in non-structural environment and better robustness with various forms.