An image rendering-based identification method for apples with different growth forms

Abstract

Identifying apples with different growth forms is an urgent task to be solved by harvesting robots on the path to intelligence. A method based on Mask RCNN + PointRend was proposed for identifying apples with different growth forms in the orchard in the work. The SPPF structure and CA attention mechanism were introduced to the backbone network of the Mask RCNN + PointRend algorithm to improve network performance because of the complexity of the apple orchard. Meanwhile, a bottom-up pathway was introduced into the feature extraction network to enhance feature dissemination, reuse, and fitting. Finally, the number of model parameters was reduced to improve the detection speed, and the CWD algorithm was used for the knowledge distillation of the model. The improved algorithms were called Mask RCNN-Point and Mask RCNN-PointKD. The recognition accuracy of the two enhanced algorithms on the test set was 85.65 and 74.13%, respectively. Besides, average detection time per image was 93.67 and 62.1 ms, respectively. The work provides a reference for identifying apples with different growth forms.