APPLE DETECTION METHOD IN THE NATURAL ENVIRONMENT BASED ON IMPROVED YOLOv5

Abstract

To improve the accuracy of apple fruit recognition, enhance the efficiency of automatic picking robots in orchards, and provide effective visual guidance for the picking robot, a target recognition network model based on improved YOLOv5 is proposed. Firstly, the original apple images collected and the data images obtained by different data enhancement methods are used to establish a dataset of 1,879 images, and the dataset is divided into the training set and the test set under 8:2; then for the problem of low detection accuracy of apple fruits in the natural environment due to the mutual obstruction of apple fruits, this paper modifies the backbone network of YOLOv5 by adding the attention mechanism of the Transformer module, the Neck structure is changed from the original PAFPN to BiFPN that can perform two-way weighted fusion, and the Head structure adds the P2 module for shallow down sampling; finally, the recognition test is performed on the dataset, and a comparative analysis is performed according to different evaluation indexes to verify the superiority of the proposed model. The experimental results show that: compared with other existing models and the single-structure improved YOLOv5 model, the comprehensive improved model proposed in this paper has higher detection accuracy, resulting in an increase of 3.7% in accuracy.