Target detection method and system for automobile aided driving
The invention discloses a target detection method and system for automobile aided driving, and relates to the field of automobile aided driving, a target detection model is obtained through a yolov5 model, a target object on a traffic road image captured in the automobile driving process is detected...
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| creator | CHEN MIAO LI KAIPENG LYU LONGJIN YANG YUEPING FAN LIANGZHONG LUO LIHUA LIN WENYU WU HAO |
| description | The invention discloses a target detection method and system for automobile aided driving, and relates to the field of automobile aided driving, a target detection model is obtained through a yolov5 model, a target object on a traffic road image captured in the automobile driving process is detected through the target detection model, and due to the fact that the yolov5 model belongs to a one-stage algorithm, the target object can be detected through the yolov5 model. Compared with R-CNN and Fast-RCNN, the method has the advantages that a candidate frame screening process is omitted, the position coordinates of the target frame and the classification probability of the target are directly regressed, and compared with R-CNN and Fast-RCNN, a two-stage algorithm of firstly screening the candidate frame, then judging whether the to-be-detected target is in the candidate frame and correcting the position of the target is needed, so that higher recognition efficiency is achieved; the method is suitable for the driv |
| format | Patent |
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Compared with R-CNN and Fast-RCNN, the method has the advantages that a candidate frame screening process is omitted, the position coordinates of the target frame and the classification probability of the target are directly regressed, and compared with R-CNN and Fast-RCNN, a two-stage algorithm of firstly screening the candidate frame, then judging whether the to-be-detected target is in the candidate frame and correcting the position of the target is needed, so that higher recognition efficiency is achieved; the method is suitable for the driv</description><language>chi ; eng</language><subject>CALCULATING ; 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=20230124&DB=EPODOC&CC=CN&NR=115641558A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,309,781,886,25569,76552</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230124&DB=EPODOC&CC=CN&NR=115641558A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>CHEN MIAO</creatorcontrib><creatorcontrib>LI KAIPENG</creatorcontrib><creatorcontrib>LYU LONGJIN</creatorcontrib><creatorcontrib>YANG YUEPING</creatorcontrib><creatorcontrib>FAN LIANGZHONG</creatorcontrib><creatorcontrib>LUO LIHUA</creatorcontrib><creatorcontrib>LIN WENYU</creatorcontrib><creatorcontrib>WU HAO</creatorcontrib><title>Target detection method and system for automobile aided driving</title><description>The invention discloses a target detection method and system for automobile aided driving, and relates to the field of automobile aided driving, a target detection model is obtained through a yolov5 model, a target object on a traffic road image captured in the automobile driving process is detected through the target detection model, and due to the fact that the yolov5 model belongs to a one-stage algorithm, the target object can be detected through the yolov5 model. 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Compared with R-CNN and Fast-RCNN, the method has the advantages that a candidate frame screening process is omitted, the position coordinates of the target frame and the classification probability of the target are directly regressed, and compared with R-CNN and Fast-RCNN, a two-stage algorithm of firstly screening the candidate frame, then judging whether the to-be-detected target is in the candidate frame and correcting the position of the target is needed, so that higher recognition efficiency is achieved; the method is suitable for the driv</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTING COUNTING PHYSICS |
| title | Target detection method and system for automobile aided driving |
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