Traffic sign detection method based on improved VFNet algorithm
The invention discloses a traffic sign detection method based on an improved VFNet algorithm. The method comprises the following steps that (1) a traffic sign detection data set is prepared, the traffic sign detection data set comprises a training set and a test set, and format conversion is carried...
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| creator | XIAO CUNJUN LI HAIBIN LIANG WENCAN LI YAQIAN ZHANG WENMING |
| description | The invention discloses a traffic sign detection method based on an improved VFNet algorithm. The method comprises the following steps that (1) a traffic sign detection data set is prepared, the traffic sign detection data set comprises a training set and a test set, and format conversion is carried out on the traffic sign detection data set to convert the traffic sign detection data set into an MSCOCO format; (2) building an improved VFNet network model; (3) training in a training set of the traffic sign detection data set by using an improved VFNet network, and storing an optimal model; and (4) inputting the test set into the optimal model which is trained and stored for testing, and verifying the detection effect of the improved model. The improved VFNet network provided by the invention can improve the detection precision of traffic sign detection.
本专利公开了一种基于改进VFNet算法的交通标志检测方法。其包含如下步骤：(1)准备交通标志检测数据集，交通标志检测数据集包含训练集和测试集两个部分，并对交通标志检测数据集进行格式转换，转换为MSCOCO格式；(2)搭建改进后的VFNet网络模型；(3)使用改进后的VFNet网络在交通标志检测数据集的训练集进行训练， |
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本专利公开了一种基于改进VFNet算法的交通标志检测方法。其包含如下步骤：(1)准备交通标志检测数据集，交通标志检测数据集包含训练集和测试集两个部分，并对交通标志检测数据集进行格式转换，转换为MSCOCO格式；(2)搭建改进后的VFNet网络模型；(3)使用改进后的VFNet网络在交通标志检测数据集的训练集进行训练，</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&date=20230317&DB=EPODOC&CC=CN&NR=115810183A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25543,76293</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230317&DB=EPODOC&CC=CN&NR=115810183A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>XIAO CUNJUN</creatorcontrib><creatorcontrib>LI HAIBIN</creatorcontrib><creatorcontrib>LIANG WENCAN</creatorcontrib><creatorcontrib>LI YAQIAN</creatorcontrib><creatorcontrib>ZHANG WENMING</creatorcontrib><title>Traffic sign detection method based on improved VFNet algorithm</title><description>The invention discloses a traffic sign detection method based on an improved VFNet algorithm. The method comprises the following steps that (1) a traffic sign detection data set is prepared, the traffic sign detection data set comprises a training set and a test set, and format conversion is carried out on the traffic sign detection data set to convert the traffic sign detection data set into an MSCOCO format; (2) building an improved VFNet network model; (3) training in a training set of the traffic sign detection data set by using an improved VFNet network, and storing an optimal model; and (4) inputting the test set into the optimal model which is trained and stored for testing, and verifying the detection effect of the improved model. The improved VFNet network provided by the invention can improve the detection precision of traffic sign detection.
本专利公开了一种基于改进VFNet算法的交通标志检测方法。其包含如下步骤：(1)准备交通标志检测数据集，交通标志检测数据集包含训练集和测试集两个部分，并对交通标志检测数据集进行格式转换，转换为MSCOCO格式；(2)搭建改进后的VFNet网络模型；(3)使用改进后的VFNet网络在交通标志检测数据集的训练集进行训练，</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>eNrjZLAPKUpMS8tMVijOTM9TSEktSU0uyczPU8hNLcnIT1FISixOTVEA8jNzC4ryy4DsMDe_1BKFxJz0_KLMkoxcHgbWtMSc4lReKM3NoOjmGuLsoZtakB-fWlyQmJyal1oS7-xnaGhqYWhgaGHsaEyMGgBuizCa</recordid><startdate>20230317</startdate><enddate>20230317</enddate><creator>XIAO CUNJUN</creator><creator>LI HAIBIN</creator><creator>LIANG WENCAN</creator><creator>LI YAQIAN</creator><creator>ZHANG WENMING</creator><scope>EVB</scope></search><sort><creationdate>20230317</creationdate><title>Traffic sign detection method based on improved VFNet algorithm</title><author>XIAO CUNJUN ; LI HAIBIN ; LIANG WENCAN ; LI YAQIAN ; ZHANG WENMING</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN115810183A3</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>XIAO CUNJUN</creatorcontrib><creatorcontrib>LI HAIBIN</creatorcontrib><creatorcontrib>LIANG WENCAN</creatorcontrib><creatorcontrib>LI YAQIAN</creatorcontrib><creatorcontrib>ZHANG WENMING</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>XIAO CUNJUN</au><au>LI HAIBIN</au><au>LIANG WENCAN</au><au>LI YAQIAN</au><au>ZHANG WENMING</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Traffic sign detection method based on improved VFNet algorithm</title><date>2023-03-17</date><risdate>2023</risdate><abstract>The invention discloses a traffic sign detection method based on an improved VFNet algorithm. The method comprises the following steps that (1) a traffic sign detection data set is prepared, the traffic sign detection data set comprises a training set and a test set, and format conversion is carried out on the traffic sign detection data set to convert the traffic sign detection data set into an MSCOCO format; (2) building an improved VFNet network model; (3) training in a training set of the traffic sign detection data set by using an improved VFNet network, and storing an optimal model; and (4) inputting the test set into the optimal model which is trained and stored for testing, and verifying the detection effect of the improved model. The improved VFNet network provided by the invention can improve the detection precision of traffic sign detection.
本专利公开了一种基于改进VFNet算法的交通标志检测方法。其包含如下步骤：(1)准备交通标志检测数据集，交通标志检测数据集包含训练集和测试集两个部分，并对交通标志检测数据集进行格式转换，转换为MSCOCO格式；(2)搭建改进后的VFNet网络模型；(3)使用改进后的VFNet网络在交通标志检测数据集的训练集进行训练，</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
| title | Traffic sign detection method based on improved VFNet algorithm |
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