In recent years, computer vision and virtual reality have developed rapidly, and 3D modeling has gradually become an important topic in computer graphics research. With the application of 3D modeling in many occasions, the demand for real-time modeling is gradually emerging. Therefore, the practical application poses a greater challenge to the computational complexity and robustness of 3D modeling This paper mainly discusses the key technology and innovative research of three-dimensional modeling based on torus in packaging structure design. 

Research on Three-Dimensional Modeling and Algorithm of Torus in Packaging Structure Design

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.

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. 

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The application of augmented reality technology in urban greening plant growth state detection

In order to solve the problems of large error and low efficiency in traditional carton packaging structure design method, augmented reality technology is applied to carton packaging structure design. Based on the camera system of augmented reality technology, the mathematical model of carton packaging structure is constructed, and the coordinate transformation of carton packaging parameters is realized. According to the results of coordinate transformation, the structure tree model of carton packaging structure was constructed, and the carton packaging structure design was completed by deleting face nodes. The experimental results show that the proposed carton packaging structure design method has low edge packaging error and can maintain high packaging efficiency. Therefore, the method of applying augmented reality technology to carton packaging structure design is of high practical value.

Shaoqiong Xia

Papers

Application of augmented reality technology in carton packaging structure design