基于改进Mask R-CNN的植物表型智能检测算法

针对高通量自动化获取的植物表型性状图像的智能检测问题，采用迁移学习和改进掩膜区域卷积神经网络(Mask R-CNN)设计植物表型智能检测分割算法。首先对残差网络进行优化，并利用特征金字塔网络(FPN)对输入图像进行特征提取；然后调整候选区域提取网络(RPN)中锚框的长宽比例和阈值，并在RoIAlign中通过双线性插值法保留了特征图的空间信息；最后改进Mask检测头，并增加特征融合机制以获得高质量的掩膜。在西瓜突变体生长情况的性状表型数据集上进行训练和检测，得到结果表明：改进后的Mask R-CNN表现出更优的检测性能，与传统Mask R-CNN相比，检测精度提高22%，掩膜准确率提高2.7%，检测时间减少42 ms，为提升农业精准化水平和推动智慧农业发展提供了技术支撑。

Intelligent detection algorithm of plant phenotype based on improved Mask R-CNN

For the intelligent detection problem of high throughput automatic acquisition of plant phenotype images, an intelligent plant phenotype detection and segmentation algorithm was designed using transfer learning and improved Mask Region Convolution Neural Network (Mask R-CNN). Firstly, the Residual Network (ResNet) was optimized, and the Feature Pyramid Network (FPN) was used to extract the features of the input image. Then, the length to width ratio and threshold of the anchor box in the network (RPN) of candidate area was adjusted, and the spatial information of the feature map was retained by bilinear interpolation in RoIAlign. Finally, the mask detection head was improved and the feature fusion mechanism was added to obtain high quality masks. The character phenotype data set of watermelon mutant growth was trained and detected, and the results showed that the improved Mask R-CNN showed better detection performance. Compared with the traditional Mask R-CNN, detection accuracy was improved by 22%, mask accuracy was improved by 2.7%, and detection time was reduced by 42 ms, which provided technical support for improving the level of agricultural precision and promoting the development of intelligent agriculture.