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Cotton seedling counting algorithm based on semantic guidance

Published: 28 June 2024 Publication History

Abstract

Abstract: To solve the problem of low efficiency, time-consuming and laborious manual statistics of cotton seedling numbers, a cotton seedling counting algorithm based on semantic guidance was proposed. The algorithm consisted of a VGG-16 backbone network, a counting branch, and a segmentation branch. The VGG-16 backbone network is used to extract the features of the input image, segmentation branch as semantic guidance is assisted in counting branch learning, and the counting branch is used to generate predicted density map and obtain the number of seedlings in the image. On the cotton seedling dataset, compared to the best performance contrast counting algorithm, the proposed algorithm reduced MAE by 22%, RMSE by 17%, rMAE by 1.31%, rRMSE by 0.56%, and increased <Formula format="inline"><TexMath><?TeX ${{\boldsymbol{R}}}^2$ ?></TexMath><File name="a00--inline1" type="gif"/></Formula> by nearly 1%.

References

[1]
Yu Ying, Zhu Huilin, Qian Jin, Survey on Deep Learning Based Crowd Counting[J]. Journal of Computer Research and Development, 2021, 58(12): 2724-2747.
[2]
Yuan Jian, Wang Shanshan, Luo Yingwei. Public place crowd counting model based on image field division[J]. Application Research of Computers, 2021, 38(4): 1256-1260.
[3]
Wang B, Liu H, Samaras D, Distribution matching for crowd counting[J]. Advances in neural information processing systems, 2020, 33: 1595-1607.
[4]
Dwibedi D, Aytar Y, Tompson J, Counting out time: Class agnostic video repetition counting in the wild [C]// Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Piscataway, NJ:IEEE Press, 2020: 10387-10396.
[5]
Yang Y, Li G, Wu Z, Reverse perspective network for perspective-aware object counting [C]// Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Piscataway, NJ:IEEE Press, 2020: 4374-4383.
[6]
Lu H, Cao Z. TasselNetV2+: A fast implementation for high-throughput plant counting from high-resolution RGB imagery[J]. Frontiers in Plant Science, 2020, 11: 1929-1943.
[7]
Xiong H, Lu H, Liu C, From open set to closed set: Counting objects by spatial divide and conquer [C]// Proceedings of the IEEE/CVF International Conference on Computer Vision. Piscataway, NJ: IEEE Press, 2019: 8362-8371.
[8]
Liu Tao, Sun Chengming, Wang Lijian, et. al. In-field Wheatear Counting Based on Image Processing Technology[J]. Transactions of the Chinese Society for Agricultural Machinery, 2014, 45(2): 282-290.
[9]
Tu S, Pang J, Liu H, Passion fruit detection and counting based on multiple scale faster R-CNN using RGB-D images[J]. Precision Agriculture, 2020, 21: 1072-1091.
[10]
Chen K, Loy C C, Gong S, Feature mining for localised crowd counting [C]// British Machine Vision Conference. Surrey, UK: British Machine Vision Association 2012, 1(2): 3.
[11]
Idrees H, Saleemi I, Seibert C, Multi-source multi-scale counting in extremely dense crowd images [C]// Proceedings of the IEEE conference on computer vision and pattern recognition. Piscataway, NJ:IEEE Press, 2013: 2547-2554.
[12]
Ryan D, Denman S, Fookes C, Crowd counting using multiple local features [C]// 2009 digital image computing: techniques and applications. Melbourne, Australia: IEEE, 2009: 81-88.
[13]
Li Y, Zhang X, Chen D. Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes [C]// Proceedings of the IEEE conference on computer vision and pattern recognition. Piscataway, NJ:IEEE Press, 2018: 1091-1100.
[14]
Liu, Y., Liu, L., Wang, P., Zhang, P., Lei, Y. Semi-supervised Crowd Counting via Self-training on Surrogate Tasks [C]. Proceedings of European Conference on Computer Vision, 2020:242-259.
[15]
Xu Tao, Duan Yinong, Du Jiahao, Crowd Counting Method Based on Multi-Scale Enhanced Network[J]. Journal of Electronics & Information Technology, 2021, 43(6): 1764-1771.
[16]
Zhang Hong, Fan Zizhu, Shi Linrui, A Head Detection Method Based on Multi-Scale Feature Fusion[J] Journal of East China Jiaotong University, 2021, 38(2): 116-121.
[17]
Song Q, Wang C, Wang Y, To choose or to fuse? scale selection for crowd counting [C]// Proceedings of the AAAI conference on artificial intelligence. Virtual:Association for the Advancement of Artificial Intelligence, 2021, 35(3): 2576-2583.
[18]
Wang P, Gao C, Wang Y, MobileCount: An efficient encoder-decoder framework for real-time crowd counting[J]. Neurocomputing, 2020, 407: 292-299.
[19]
Sandler M, Howard A, Zhu M, Mobilenetv2: Inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Piscataway, NJ: IEEE Press, 2018: 4510-4520.
[20]
Marsden M, McGuinness K, Little S, People, penguins and petri dishes: Adapting object counting models to new visual domains and object types without forgetting[C] //Proceedings of the IEEE conference on computer vision and pattern recognition. Piscataway, NJ:IEEE Press, 2018: 8070-8079.
[21]
Xiong H, Cao Z, Lu H, TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks[J]. Plant Methods, 2019, 15(1): 1-14.
[22]
Lu H, Cao Z, Xiao Y, TasselNet: counting maize tassels in the wild via local counts regression network[J]. Plant Methods, 2017, 13(1): 1-17.
[23]
Koh J C O, Hayden M, Daetwyler H, Estimation of crop plant density at early mixed growth stages using UAV imagery[J]. Plant Methods, 2019, 15(1): 1-9.
[24]
Valente J, Sari B, Kooistra L, Automated crop plant counting from very high-resolution aerial imagery[J]. Precision Agriculture, 2020, 21: 1366-1384.
[25]
Samiei S, Rasti P, Ly Vu J, Deep learning-based detection of seedling development[J]. Plant Methods, 2020, 16(1): 1-11.
[26]
Liu L, Lu H, Li Y, High-throughput rice density estimation from transplantation to tillering stages using deep networks[J]. Plant Phenomics, 2020, 2: 1-14.

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  1. Cotton seedling counting algorithm based on semantic guidance

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    ICRSA '23: Proceedings of the 2023 6th International Conference on Robot Systems and Applications
    September 2023
    335 pages
    ISBN:9798400708039
    DOI:10.1145/3655532
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 28 June 2024

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    Author Tags

    1. Computer Vision
    2. Cotton Seedling
    3. Object Counting

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