Research on pepper recognition method in complex environment based on Mask R-CNN
In order to solve the problem that pepper picking robots can not pick pepper accurately in real scenes due to pepper clusters,adhesion and uneven lighting,a pepper recognition method based on Mask R-CNN instance segmentation network model is proposed.With pepper in the real scene as the research object,4 496 images of naturally growing pepper were collected,and 4 000 of them were labeled as data sets.The data sets were trained by setting different learning rates,training cycles and model network layers.The experimental results show that the Mask R-CNN network model has a good effect on pepper recognition and segmentation in the real scene,with an average accuracy of 90.34%and an average speed of 0.82 s/frame,providing a strong technical support for pepper segmentation recognition and location of intelligent pepper picking robot.
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