Ship target detection method based on improved DCN

The invention discloses a ship target detection method based on an improved DCN, and the method comprises the steps: selecting a remote sensing image data set with a marking file containing direction information as an input image, and carrying out the random overturning and filling of the input imag...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: XU SHUFANG, MAO YINGCHI, WANG LONGBAO, XU HUIHUA, SHEN YICAN, ZHANG XUEJIE, GAO HONGMIN, CHU HONGQIANG
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 XU SHUFANG
MAO YINGCHI
WANG LONGBAO
XU HUIHUA
SHEN YICAN
ZHANG XUEJIE
GAO HONGMIN
CHU HONGQIANG
description The invention discloses a ship target detection method based on an improved DCN, and the method comprises the steps: selecting a remote sensing image data set with a marking file containing direction information as an input image, and carrying out the random overturning and filling of the input image; preprocessing the input image, and dividing the input image into a training set, a verification set and a test set; inputting the training set into the improved DCN model for training; inputting the test set into the trained improved DCN model, detecting the remote sensing image to obtain a directed bounding box, and detecting a ship target; according to the invention, the DCN is adopted to obtain the offset of the point set, the APAM module is adopted to screen positive samples, the SPP module is introduced to realize the fusion between local features and global features, the correlation of classification and positioning is further enhanced, the acquisition of high-quality samples is ensured, and the high-preci
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN116740568A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN116740568A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN116740568A3</originalsourceid><addsrcrecordid>eNrjZDAKzsgsUChJLEpPLVFISS1JTS7JzM9TyE0tychPUUhKLE5NUQDyM3MLivLLgGwXZz8eBta0xJziVF4ozc2g6OYa4uyhm1qQH59aXJCYnJqXWhLv7GdoaGZuYmBqZuFoTIwaAI5iK3c</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Ship target detection method based on improved DCN</title><source>esp@cenet</source><creator>XU SHUFANG ; MAO YINGCHI ; WANG LONGBAO ; XU HUIHUA ; SHEN YICAN ; ZHANG XUEJIE ; GAO HONGMIN ; CHU HONGQIANG</creator><creatorcontrib>XU SHUFANG ; MAO YINGCHI ; WANG LONGBAO ; XU HUIHUA ; SHEN YICAN ; ZHANG XUEJIE ; GAO HONGMIN ; CHU HONGQIANG</creatorcontrib><description>The invention discloses a ship target detection method based on an improved DCN, and the method comprises the steps: selecting a remote sensing image data set with a marking file containing direction information as an input image, and carrying out the random overturning and filling of the input image; preprocessing the input image, and dividing the input image into a training set, a verification set and a test set; inputting the training set into the improved DCN model for training; inputting the test set into the trained improved DCN model, detecting the remote sensing image to obtain a directed bounding box, and detecting a ship target; according to the invention, the DCN is adopted to obtain the offset of the point set, the APAM module is adopted to screen positive samples, the SPP module is introduced to realize the fusion between local features and global features, the correlation of classification and positioning is further enhanced, the acquisition of high-quality samples is ensured, and the high-preci</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; PHYSICS</subject><creationdate>2023</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&amp;date=20230912&amp;DB=EPODOC&amp;CC=CN&amp;NR=116740568A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25563,76318</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20230912&amp;DB=EPODOC&amp;CC=CN&amp;NR=116740568A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>XU SHUFANG</creatorcontrib><creatorcontrib>MAO YINGCHI</creatorcontrib><creatorcontrib>WANG LONGBAO</creatorcontrib><creatorcontrib>XU HUIHUA</creatorcontrib><creatorcontrib>SHEN YICAN</creatorcontrib><creatorcontrib>ZHANG XUEJIE</creatorcontrib><creatorcontrib>GAO HONGMIN</creatorcontrib><creatorcontrib>CHU HONGQIANG</creatorcontrib><title>Ship target detection method based on improved DCN</title><description>The invention discloses a ship target detection method based on an improved DCN, and the method comprises the steps: selecting a remote sensing image data set with a marking file containing direction information as an input image, and carrying out the random overturning and filling of the input image; preprocessing the input image, and dividing the input image into a training set, a verification set and a test set; inputting the training set into the improved DCN model for training; inputting the test set into the trained improved DCN model, detecting the remote sensing image to obtain a directed bounding box, and detecting a ship target; according to the invention, the DCN is adopted to obtain the offset of the point set, the APAM module is adopted to screen positive samples, the SPP module is introduced to realize the fusion between local features and global features, the correlation of classification and positioning is further enhanced, the acquisition of high-quality samples is ensured, and the high-preci</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>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZDAKzsgsUChJLEpPLVFISS1JTS7JzM9TyE0tychPUUhKLE5NUQDyM3MLivLLgGwXZz8eBta0xJziVF4ozc2g6OYa4uyhm1qQH59aXJCYnJqXWhLv7GdoaGZuYmBqZuFoTIwaAI5iK3c</recordid><startdate>20230912</startdate><enddate>20230912</enddate><creator>XU SHUFANG</creator><creator>MAO YINGCHI</creator><creator>WANG LONGBAO</creator><creator>XU HUIHUA</creator><creator>SHEN YICAN</creator><creator>ZHANG XUEJIE</creator><creator>GAO HONGMIN</creator><creator>CHU HONGQIANG</creator><scope>EVB</scope></search><sort><creationdate>20230912</creationdate><title>Ship target detection method based on improved DCN</title><author>XU SHUFANG ; MAO YINGCHI ; WANG LONGBAO ; XU HUIHUA ; SHEN YICAN ; ZHANG XUEJIE ; GAO HONGMIN ; CHU HONGQIANG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN116740568A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</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>XU SHUFANG</creatorcontrib><creatorcontrib>MAO YINGCHI</creatorcontrib><creatorcontrib>WANG LONGBAO</creatorcontrib><creatorcontrib>XU HUIHUA</creatorcontrib><creatorcontrib>SHEN YICAN</creatorcontrib><creatorcontrib>ZHANG XUEJIE</creatorcontrib><creatorcontrib>GAO HONGMIN</creatorcontrib><creatorcontrib>CHU HONGQIANG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>XU SHUFANG</au><au>MAO YINGCHI</au><au>WANG LONGBAO</au><au>XU HUIHUA</au><au>SHEN YICAN</au><au>ZHANG XUEJIE</au><au>GAO HONGMIN</au><au>CHU HONGQIANG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Ship target detection method based on improved DCN</title><date>2023-09-12</date><risdate>2023</risdate><abstract>The invention discloses a ship target detection method based on an improved DCN, and the method comprises the steps: selecting a remote sensing image data set with a marking file containing direction information as an input image, and carrying out the random overturning and filling of the input image; preprocessing the input image, and dividing the input image into a training set, a verification set and a test set; inputting the training set into the improved DCN model for training; inputting the test set into the trained improved DCN model, detecting the remote sensing image to obtain a directed bounding box, and detecting a ship target; according to the invention, the DCN is adopted to obtain the offset of the point set, the APAM module is adopted to screen positive samples, the SPP module is introduced to realize the fusion between local features and global features, the correlation of classification and positioning is further enhanced, the acquisition of high-quality samples is ensured, and the high-preci</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language chi ; eng
recordid cdi_epo_espacenet_CN116740568A
source esp@cenet
subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
title Ship target detection method based on improved DCN
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T16%3A34%3A57IST&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=XU%20SHUFANG&rft.date=2023-09-12&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN116740568A%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