Data set construction method of virtual assembly classification detection network for agricultural machinery

LI Cheng’en; ZOU Xiangjun; LIAN Guoping; LIN Junqiang; ZHANG Po; CHEN Mingyou; YE Lei

doi:10.7671/j.issn.1001-411X.202104005

Journal of South China Agricultural University > 2021 > 42(6): 117-125. > DOI: 10.7671/j.issn.1001-411X.202104005

LI Cheng’en, ZOU Xiangjun, LIAN Guoping, et al. Data set construction method of virtual assembly classification detection network for agricultural machinery[J]. Journal of South China Agricultural University, 2021, 42(6): 117-125. DOI: 10.7671/j.issn.1001-411X.202104005

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Data set construction method of virtual assembly classification detection network for agricultural machinery

College of Engineering, South China Agricultural University, Guangzhou 510642, China

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Received Date: April 06, 2021
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
Virtual assembly can save production cost and improve the production efficiency of mechanical parts in industry. Due to the lack of the function of automatically creating collision body, the existing virtual reality engine can not completely restore the physical properties in the actual assembly process. Universaly building mechanical part grid entity is the important approach for improving practicability, accuracy and universality of virtual assembly.

Method
The batch preprocessing algorithm and the improved sampling algorithm were designed for the batch agricultural machinery parts sampling. The image data set of three-dimensional model sample was constructed to train the artificial intelligence classification detection network, which can classify and detect the relevant parameters from the image samples, and realize the function of automatic construction of collision body.

Result
The accuracy of the classification network trained by the optimized algorithm was more than 98% for the classification of parts from pictures, and more than 98% for the construction parameters of collision bodies from pictures. However, the network without optimized training did not converge.

Conclusion
This method can effectively improve the recognition accuracy and training efficiency of the artificial intelligence classification detection network, and improve the modeling accuracy of the collision body by combining with the collision body parameterization construction program.
- virtual assembly,
- artificial intelligence,
- collision body,
- batching,
- data set

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References (25)

References

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Data set construction method of virtual assembly classification detection network for agricultural machinery