Unsupervised Domain Adaptation for DNN-based Automated Harvesting

Computer vision systems based on convolutional neural networks are being rapidly introduced in the field of precision agriculture to solve the problem of scene recognition. Convolutional networks allow performing high-precision recognition, but a significant problem is the expensive process of adapting the network to new conditions. This article proposes a method of fast adaptation of the trained network to minor changes in the source domain without annotating new data. This method is known as Adversarial Domain Adaptation, in the current paper it is applied to the problem of agricultural scene recognition in automated harvesting. The initial training procedure is modified for parallel training of an additional subnet on unannotated data, which makes it possible to compensate the domain shift due to adversarial training. This approach allows us to monotonically increase the quality of all recognized classes of objects and to enhance the stability of CNN model.