高通量菌落挑选仪是生物制药行业中菌种筛选的重要设备，但其只能识别二维位置信息。为了解决菌落的三维信息提取问题，提出基于迁移学习的单目成像菌落深度提取算法。该算法以残差网络为基础，结合多尺度的网络结构提取特征，采用无监督的迁移学习训练方式，使网络能够估计菌落深度信息。实验结果表明，该算法的平均相对误差为0.171，均方根误差为6.198，对数均方根误差为0.256，在1.25阈值下的预测准确率提高到了76.4%，算法能够同时获取菌落深度信息及其表面特征，为进一步提高筛选精度和有效挑选菌落提供了参考。

High-throughput colony sorter is an important equipment for bacteria screening in the biopharmaceutical industry. It uses colony image for intelligent identification and selection, but at present, the equipment only recognizes two-dimensional location information. In order to solve the problem of three-dimensional colony information extraction, this paper proposes a monocular image colony depth extraction algorithm based on transfer learning. The algorithm is based on residual network, combined with multi-scale network structure to extract features, and adopts unsupervised transfer learning training mode, so that the network can estimate the colony depth information. The experimental results show that the average relative error of the algorithm is 0.171, the root mean square error is 6.198, and the log root mean square error is 0.256. The accuracy of the results under the threshold value of 1.25 is increased to 76.4%. The algorithm can obtain the depth information and surface characteristics of the colony at the same time, which provides a referencefor further improving the screening accuracy and effectively selecting the colony.