A method of log diameter measurement based on instance segmentation model

Li Jiayu; Liu Jinhao

doi:10.12171/j.1000-1522.20220345

Journal of Beijing Forestry University > 2023 > 45(3): 153-159. > DOI: 10.12171/j.1000-1522.20220345

Li Jiayu, Liu Jinhao. A method of log diameter measurement based on instance segmentation model[J]. Journal of Beijing Forestry University, 2023, 45(3): 153-159. DOI: 10.12171/j.1000-1522.20220345

Citation:

Li Jiayu, Liu Jinhao. A method of log diameter measurement based on instance segmentation model[J]. Journal of Beijing Forestry University, 2023, 45(3): 153-159. DOI: 10.12171/j.1000-1522.20220345

Citation:

Li Jiayu, Liu Jinhao. A method of log diameter measurement based on instance segmentation model[J]. Journal of Beijing Forestry University, 2023, 45(3): 153-159. DOI: 10.12171/j.1000-1522.20220345

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A method of log diameter measurement based on instance segmentation model

Li Jiayu,
Liu Jinhao^,

School of Technology, Key Laboratory of Forestry Equipment and Automation of National Forestry andGrassland Administration, Beijing 100083, China

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Received Date: August 18, 2022
Revised Date: February 08, 2023
Available Online: February 14, 2023
Published Date: March 24, 2023

Graphical Abstract

Abstract

Abstract

Objective In order to reduce the influence of human factors on the log sizing results and improve the work efficiency, a log sizing method based on mask region instance segmentation model and edge fitting algorithm was proposed.
Method The method used monocular camera as the acquisition equipment, and the end face images of eucalyptus with three different diameter classes and rectangular scaleplate were taken as the research objects. Firstly, the image was collected at different distances to make a data set, and the training set, verification set and test set were divided at the ratio of 8∶1∶1, and the log end face recognition experiment database was established. Secondly, the instance segmentation model was used to extract the end face part to generate the mask, the edge fitting algorithm was used to obtain the pixel size of the rectangular scaleplate and the log end face, combined the actual size of the scaleplate to obtain the actual size of the log end face. Finally, the error of fitting size of the algorithm and the error of different national standard volume calculation formulas were compared.
Result It was found that the example segmentation model in this paper can achieve more accurate mask segmentation of log end face, and the accuracy and recall rate were 99.89% and 99.41%, respectively. Compared with the one-stage algorithm, F1 scores and mean average precision were significantly improved. By fitting the end face to an ellipse using a least square edge fitting algorithm, the short diameter of the ellipse was obtained as the log diameter. Compared with the real value, the average percentage error was about −2.00%, which was slightly smaller than the real value. Comparing the error of logs with different sizes, the measurement error of 100% small size logs, 98% medium size logs and 95% large size logs was −5%−5%. By comparing the log end face images collected at different distances, it was found that the best effect was to collect images within 50−100 cm, the average relative error didn’t exceed −2.22%, and the error gradually increased when the distance was greater than 100 cm. By comparing the calculation standards of log volume in different countries, the volume error was −4.5% according to the calculation formula of wood volume in China, which was the lowest among the four national standards of China, America, Russia and Japan.
Conclusion Compared with manual measuring, the measuring method proposed in this paper is more efficient, less affected by human factors, and can more accurately measure the log size, and achieve the goal of replacing manual log sizing operations.
- log scale,
- monocular vision,
- instance segmentation,
- edge fitting algorithm

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References (22)

References

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A method of log diameter measurement based on instance segmentation model