http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-110246141-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10004 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20016 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-194 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-11 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-194 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-11 |
| filingDate | 2019-06-13-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2022-10-21-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2022-10-21-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-110246141-B |
| titleOfInvention | A Vehicle Image Segmentation Method in Complex Traffic Scenes Based on Joint Corner Pooling |
| abstract | The invention provides a vehicle image segmentation method in complex traffic scenes based on joint corner pooling, which re-integrates the CamVid data set, extracts the features of the data set by the hourglass network, and processes the features respectively by the foreground segmentation branch and the background segmentation branch. In the foreground segmentation branch, the features first enter the multi-target corner pooling module to obtain the target candidate frame, target category label and region of interest, and use the mask scanning module to scan the accurate mask of the target; in the background segmentation branch, The feature map is fused with the region of interest generated by the multi-object corner pooling module, and the background map is generated by the semantic segmentation module. The mask, target category and candidate frame generated by the foreground segmentation branch and the background image generated by the background segmentation branch are sorted and positioned in the front-background sorting module to generate the panoramic segmentation result. It solves the problem that the existing technology often performs poorly when detecting vehicles in complex traffic scenarios, and cannot accurately detect and frame these vehicles one by one. |
| priorityDate | 2019-06-13-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID456171974 |
Total number of triples: 17.