MFNet: Multi-scale feature enhancement networks for wheat head detection and counting in complex scene
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Published:2024-10
Issue:
Volume:225
Page:109342
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ISSN:0168-1699
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Container-title:Computers and Electronics in Agriculture
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language:en
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Short-container-title:Computers and Electronics in Agriculture
Author:
Qian YurongORCID, Qin Yugang, Wei Hongyang, Lu Yiguo, Huang Yuning, Liu Peng, Fan Yingying
Reference41 articles.
1. Bhagat, S., Kokare, M., Haswani, V., Hambarde, P., Kamble, R., 2021. WheatNet-lite: a novel light weight network for wheat head detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1332–1341. 2. YOLOv4: Optimal speed and accuracy of object detection;Bochkovskiy,2020 3. Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y., 2017. Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 764–773. 4. Histograms of oriented gradients for human detection;Dalal,2005 5. 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;David;Plant Phenomics,2020
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