Remote sensing image target detection method
The invention discloses a remote sensing image target detection method, which comprises the steps of collecting a remote sensing image data set, labeling the data set, making a remote sensing image data set in a YOLO format, and dividing the remote sensing image data set into a training set and a te...
Gespeichert in:
Hauptverfasser: | , , , , , , |
---|---|
Format: | Patent |
Sprache: | chi ; eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | |
container_volume | |
creator | CHEN XIN WANG SHUOYANG SHAN HUILIN DUAN XIUXIAN CHEN QIANHANG LI CHANGSHUAI ZHANG YANHAO |
description | The invention discloses a remote sensing image target detection method, which comprises the steps of collecting a remote sensing image data set, labeling the data set, making a remote sensing image data set in a YOLO format, and dividing the remote sensing image data set into a training set and a test set; a multi-scale neural network based on an enhanced receptive field is established, the multi-scale neural network is mainly composed of an input port, a backbone network, a neck network and a detection head, and the backbone network and the neck network form a main module of the multi-scale neural network for enhancing small target features; inputting the remote sensing image of the training set into a receptive field enhanced multi-scale neural network for training and verification to obtain an optimal detection model; and performing target detection on the test set by using the trained and verified optimal model. The invention provides a multi-scale remote sensing image target detection method for enhancin |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN118314434A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN118314434A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN118314434A3</originalsourceid><addsrcrecordid>eNrjZNAJSs3NL0lVKE7NK87MS1fIzE1MT1UoSSxKTy1RSEktSU0uyczPU8hNLcnIT-FhYE1LzClO5YXS3AyKbq4hzh66qQX58anFBYnJqXmpJfHOfoaGFsaGJibGJo7GxKgBAJzdKdo</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Remote sensing image target detection method</title><source>esp@cenet</source><creator>CHEN XIN ; WANG SHUOYANG ; SHAN HUILIN ; DUAN XIUXIAN ; CHEN QIANHANG ; LI CHANGSHUAI ; ZHANG YANHAO</creator><creatorcontrib>CHEN XIN ; WANG SHUOYANG ; SHAN HUILIN ; DUAN XIUXIAN ; CHEN QIANHANG ; LI CHANGSHUAI ; ZHANG YANHAO</creatorcontrib><description>The invention discloses a remote sensing image target detection method, which comprises the steps of collecting a remote sensing image data set, labeling the data set, making a remote sensing image data set in a YOLO format, and dividing the remote sensing image data set into a training set and a test set; a multi-scale neural network based on an enhanced receptive field is established, the multi-scale neural network is mainly composed of an input port, a backbone network, a neck network and a detection head, and the backbone network and the neck network form a main module of the multi-scale neural network for enhancing small target features; inputting the remote sensing image of the training set into a receptive field enhanced multi-scale neural network for training and verification to obtain an optimal detection model; and performing target detection on the test set by using the trained and verified optimal model. The invention provides a multi-scale remote sensing image target detection method for enhancin</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; PHYSICS</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240709&DB=EPODOC&CC=CN&NR=118314434A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,778,883,25547,76298</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240709&DB=EPODOC&CC=CN&NR=118314434A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>CHEN XIN</creatorcontrib><creatorcontrib>WANG SHUOYANG</creatorcontrib><creatorcontrib>SHAN HUILIN</creatorcontrib><creatorcontrib>DUAN XIUXIAN</creatorcontrib><creatorcontrib>CHEN QIANHANG</creatorcontrib><creatorcontrib>LI CHANGSHUAI</creatorcontrib><creatorcontrib>ZHANG YANHAO</creatorcontrib><title>Remote sensing image target detection method</title><description>The invention discloses a remote sensing image target detection method, which comprises the steps of collecting a remote sensing image data set, labeling the data set, making a remote sensing image data set in a YOLO format, and dividing the remote sensing image data set into a training set and a test set; a multi-scale neural network based on an enhanced receptive field is established, the multi-scale neural network is mainly composed of an input port, a backbone network, a neck network and a detection head, and the backbone network and the neck network form a main module of the multi-scale neural network for enhancing small target features; inputting the remote sensing image of the training set into a receptive field enhanced multi-scale neural network for training and verification to obtain an optimal detection model; and performing target detection on the test set by using the trained and verified optimal model. The invention provides a multi-scale remote sensing image target detection method for enhancin</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZNAJSs3NL0lVKE7NK87MS1fIzE1MT1UoSSxKTy1RSEktSU0uyczPU8hNLcnIT-FhYE1LzClO5YXS3AyKbq4hzh66qQX58anFBYnJqXmpJfHOfoaGFsaGJibGJo7GxKgBAJzdKdo</recordid><startdate>20240709</startdate><enddate>20240709</enddate><creator>CHEN XIN</creator><creator>WANG SHUOYANG</creator><creator>SHAN HUILIN</creator><creator>DUAN XIUXIAN</creator><creator>CHEN QIANHANG</creator><creator>LI CHANGSHUAI</creator><creator>ZHANG YANHAO</creator><scope>EVB</scope></search><sort><creationdate>20240709</creationdate><title>Remote sensing image target detection method</title><author>CHEN XIN ; WANG SHUOYANG ; SHAN HUILIN ; DUAN XIUXIAN ; CHEN QIANHANG ; LI CHANGSHUAI ; ZHANG YANHAO</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN118314434A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>CHEN XIN</creatorcontrib><creatorcontrib>WANG SHUOYANG</creatorcontrib><creatorcontrib>SHAN HUILIN</creatorcontrib><creatorcontrib>DUAN XIUXIAN</creatorcontrib><creatorcontrib>CHEN QIANHANG</creatorcontrib><creatorcontrib>LI CHANGSHUAI</creatorcontrib><creatorcontrib>ZHANG YANHAO</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>CHEN XIN</au><au>WANG SHUOYANG</au><au>SHAN HUILIN</au><au>DUAN XIUXIAN</au><au>CHEN QIANHANG</au><au>LI CHANGSHUAI</au><au>ZHANG YANHAO</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Remote sensing image target detection method</title><date>2024-07-09</date><risdate>2024</risdate><abstract>The invention discloses a remote sensing image target detection method, which comprises the steps of collecting a remote sensing image data set, labeling the data set, making a remote sensing image data set in a YOLO format, and dividing the remote sensing image data set into a training set and a test set; a multi-scale neural network based on an enhanced receptive field is established, the multi-scale neural network is mainly composed of an input port, a backbone network, a neck network and a detection head, and the backbone network and the neck network form a main module of the multi-scale neural network for enhancing small target features; inputting the remote sensing image of the training set into a receptive field enhanced multi-scale neural network for training and verification to obtain an optimal detection model; and performing target detection on the test set by using the trained and verified optimal model. The invention provides a multi-scale remote sensing image target detection method for enhancin</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | chi ; eng |
recordid | cdi_epo_espacenet_CN118314434A |
source | esp@cenet |
subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
title | Remote sensing image target detection method |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T07%3A57%3A43IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=CHEN%20XIN&rft.date=2024-07-09&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN118314434A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |