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基于深度学习的复杂自然环境下桑树枝干识别方法

Identification method of mulberry tree branches in complex natural environments based on deep learning

  • 摘要: 在复杂自然环境下完成桑树枝干识别是实现桑叶采摘机智能化的关键部分,针对实际应用中光照条件变化多、桑叶遮挡和桑树分枝多等问题,提出一种基于深度学习的复杂自然环境下桑树枝干识别方法。首先,采用旋转、镜像翻转、色彩增强和同态滤波的图像处理方法扩展数据集,以提高模型的鲁棒性,通过Resnet50目标检测网络模型以及相机标定获得照片中所需的桑树枝干坐标,通过试验发现当学习率设置为0.001,迭代次数设置为600时模型的识别效果最优。该方法对于复杂自然环境中的不同光照条件具有良好的适应性,能够对存在多条分支以及被桑叶遮挡的桑树枝干进行识别并获取坐标信息,识别准确率达到87.42%,可以满足实际工作需求。

     

    Abstract: Completing mulberry tree branch recognition in the complex natural environment is a critical role in implementing the intelligence of the mulberry leaf picking machine. For the challenge of many changes in lighting conditions, mulberry leaf shading, and mulberry tree branching in practical applications, this paper proposes a deep learning-based method for mulberry tree branch recognition in complex natural environments. Firstly, rotation, mirror flip, color enhancement, and homomorphic filtering were used to extend the image datasets, which could improve the robustness of the model. Then, we obtained the required coordinates of mulberry branches in the photos through Resnet50 target detection network model and camera calibration, and found that the model had the best recognition effect when the learning rate was set to 0.001 and the number of iterations was set to 600. The method has good adaptability to different lighting conditions in complex natural environments, and is able to identify and obtain coordinate information for mulberry branches with multiple branches and those shaded by mulberry leaves, with an accuracy rate of 87.42%, which can meet the actual working requirements.

     

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