Enhanced Wheat Head Detection in Images Using Fourier Domain Adaptation and Random Guided Filter
Author:
Affiliation:
1. College of Information Science and Technology, Gansu Agricultural University, Lanzhou, 730070, PR China
2. College of Plant Protection, Gansu Agricultural University, Lanzhou, 730070, PR China
Funder
2021 Lanzhou Talent Innovation and Entrepreneurship Project
2022 Ministry of Science and Technology National Foreign Expert Project
2023 Gansu Province Higher Education Industry Support Project
2023 Gansu Province Key R&D Plan
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/07038992.2024.2367479
Reference31 articles.
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3. Bochkovskiy A. Wang C.-Y. and Liao H.-Y.M. 2020. “Yolov4: Optimal speed and accuracy of object detection.”. arXiv:2004.10934.
4. Cascade R-CNN: Delving Into High Quality Object Detection
5. David E. Madec S. Sadeghi-Tehran P. Aasen H. Zheng B. Liu S. Kirchgessner N. et al. 2020. “Global Wheat Head Detection (GWHD) dataset: A large and diverse dataset of high-resolution RGB-labelled images to develop and benchmark wheat head detection methods.” Plant Phenomics Vol. 2020: pp. 1–12.
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