http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-110874598-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-23213 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-44 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-762 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-774 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-44 |
| filingDate | 2019-11-05^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2022-09-27^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2022-09-27^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-110874598-B |
| titleOfInvention | A deep learning-based method for detecting water marks on expressways |
| abstract | The invention discloses a method for detecting highway water marks based on deep learning. 3: Semantically segment the data set obtained in step 1; Step 4: fuse the segmentation results obtained in steps 3 and 4 to obtain the required highway water mark detection results; the deep learning method of the present invention combines the semantic The combination of segmentation and self-adaptive clustering segmentation can perform high-efficiency and high-precision detection of highway water marks, and can achieve good application results in highway water mark detection. |
| priorityDate | 2019-11-05^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
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