Authors

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.

Abstract in Chinese

为提高对苹果果实识别的准确率，提升果园自动采摘机器人的工作效率，为给采摘机械手提供有效的视觉引导，提出了一种基于改进YOLOv5目标识别网络模型。首先使用采集到的苹果原始图像以及其搭配不同数据增强方式得到的数据图像共1879幅建立数据集，按照8:2将数据集划分成训练集与测试集；然后针对苹果果实之间相互遮挡导致自然环境下苹果果实检测精度低的问题，本文将YOLOv5的骨干网络进行改动，增添具有注意力机制的Transformer模块，Neck结构由原来的PAFPN改成可以进行双向加权融合的BiFPN，Head结构增加了浅层下采样的P2模块；最后，对数据集进行识别测试,并根据不同评价指标进行对比分析,验证所建模型的优越性。实验结果表明：相比于其他已有模型以及单一结构改进后的YOLOv5模型，本文提出的综合改进模型具有更高的检测精度，使识别精确率提升了3.7%。