Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107111
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | - |
dc.creator | Ma, YY | - |
dc.creator | Sun, ZL | - |
dc.creator | Zeng, Z | - |
dc.creator | Lam, KM | - |
dc.date.accessioned | 2024-06-13T01:03:58Z | - |
dc.date.available | 2024-06-13T01:03:58Z | - |
dc.identifier.issn | 1545-598X | - |
dc.identifier.uri | http://hdl.handle.net/10397/107111 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Y. -Y. Ma, Z. -L. Sun, Z. Zeng and K. -M. Lam, "Corn-Plant Counting Using Scare-Aware Feature and Channel Interdependence," in IEEE Geoscience and Remote Sensing Letters, vol. 19, 2022, Art no. 2500905 is available at https://doi.org/10.1109/LGRS.2021.3049489. | en_US |
dc.subject | Channel attention module | en_US |
dc.subject | Corn-plant counting | en_US |
dc.subject | Scale-aware (SA) feature | en_US |
dc.subject | Visual Geometry Group (VGG) feature | en_US |
dc.title | Corn-plant counting using scare-aware feature and channel interdependence | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 19 | - |
dc.identifier.doi | 10.1109/LGRS.2021.3049489 | - |
dcterms.abstract | Corn-plant counting is an important process for predicting corn yield and analyzing corn-plant phenotypes. In this letter, an effective corn-plant counting method is proposed, which is based on utilizing the scale-aware (SA) contextual feature and channel interdependence (CI). Given the Visual Geometry Group (VGG) Network features, the SA features are extracted by spatial pyramid pooling to derive multiscale context information. In order to utilize the channel interdependent information, the VGG features are integrated via a channel attention module. Moreover, an encoder-decoder structure is constructed to fuse the SA features and the CI-based features. Considering the sparsity of a corn plant, a hybrid loss function is adopted to train the network, by considering a density map loss function and an absolute count loss function. Experimental results demonstrate the effectiveness of the proposed method for corn-plant counting. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE geoscience and remote sensing letters, 2022, v. 19, 2500905 | - |
dcterms.isPartOf | IEEE geoscience and remote sensing letters | - |
dcterms.issued | 2022 | - |
dc.identifier.scopus | 2-s2.0-85099729829 | - |
dc.identifier.artn | 2500905 | - |
dc.description.validate | 202403 bckw | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0100 | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 55021708 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Lam_Corn-Plant_Counting_Using.pdf | Pre-Published version | 4.42 MB | Adobe PDF | View/Open |
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