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Using GMOSTNet for Tree Detection Under Complex Illumination and Morphological Occlusion

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Data Science (ICPCSEE 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1257))

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Abstract

Trees are an integral part of the forestry ecosystem. In forestry work, the precise acquisition of tree morphological parameters and attributes is affected by complex illumination and tree morphology. In order to minimize a series of inestimable problems, such as yield reduction, ecological damage, and destruction, caused by inaccurate acquisition of tree location information, this paper proposes a ground tree detection method GMOSTNet. Based on the four types of tree species in the GMOST dataset and Faster R-CNN, it extracted the features of the trees, generate candidate regions, classification, and other operations. By reducing the influence of illumination and occlusion factors during experimentation, more detailed information of the input image was obtained. Meanwhile, regarding false detections caused by inappropriate approximations, the deviation and proximity of the proposal were adjusted. The experimental results showed that the AP value of the four tree species is improved after using GMOSTNet, and the overall accuracy increases from the original 87.25% to 93.25%.

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Acknowledgement

This work is supported by National Natural Science Foundation of China (U1809208).

Author information

Authors and Affiliations

  1. School of Information Engineering, Zhejiang A&F University, Hangzhou, China

    Zheng Qian, Hailin Feng, Yinhui Yang, Xiaochen Du & Kai Xia

  2. Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou, China

    Zheng Qian & Yinhui Yang

  3. Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou, China

    Hailin Feng, Xiaochen Du & Kai Xia

Authors
  1. Zheng Qian
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  2. Hailin Feng
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  3. Yinhui Yang
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  4. Xiaochen Du
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  5. Kai Xia
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Corresponding author

Correspondence to Hailin Feng .

Editor information

Editors and Affiliations

  1. North University of China, Taiyuan, China

    Jianchao Zeng

  2. Northeast Forestry University, Harbin, China

    Weipeng Jing

  3. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  4. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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Qian, Z., Feng, H., Yang, Y., Du, X., Xia, K. (2020). Using GMOSTNet for Tree Detection Under Complex Illumination and Morphological Occlusion. In: Zeng, J., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7981-3_36

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  • DOI: https://doi.org/10.1007/978-981-15-7981-3_36

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-7980-6

  • Online ISBN: 978-981-15-7981-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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