The Application of Augmented Reality Technology in Urban Greening Plant Growth State Detection.

The current target detection network in deep learning has been widely used in plant growth state detection. However, with the development of deep learning, within the field of plant growth state detection, the performance of the detection network is no longer the primary factor limiting the detection accuracy and model generalization ability. The construction of high-quality and large-scale plant datasets is more significant for the improvement of model detection accuracy and generalization ability. However, traditional methods for building deep learning datasets for plants have a large time span and low efficiency. And it is difficult to construct and expand the dataset for plants with complex growth environments and difficult image acquisition by existing methods. To address this problem, this paper proposes a method for constructing plant datasets based on augmented reality techniques. The method proposed in this paper allows for the rapid and efficient construction of large-scale field datasets that match the actual inspection environment in the lack of data. Meanwhile, this paper proposes an automatic annotation method for datasets in conjunction with the imaging environment in virtual space. In this paper, we experimentally compare the proposed method with the method of expanding the dataset using GAN networks. Using the virtual dataset constructed by the method proposed in this paper as the training set, the trained YOLOv5 model achieves an average accuracy (@0.5:0.95) of 0.71 for the three detection categories on the test set. The detection accuracy of the six mixed datasets constructed using the two data expansion methods on the test set was experimentally tested. The proposed method in this paper improved the accuracy by 2.2%, 3.1%, and 7.0%, respectively. The smaller the percentage of real images, the greater the accuracy improvement. Experiments show that the method proposed in this paper can well solve the problems faced in the field of plant growth state detection, such as the lack of data, and provides a new idea for the production and expansion of datasets in plant detection tasks.