http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-108710875-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30242 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V2201-08 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-22 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-24 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-52 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-462 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-46 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-00 |
| filingDate | 2018-09-11-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2019-01-08-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2019-01-08-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-108710875-B |
| titleOfInvention | A method and device for counting aerial photography highway vehicles based on deep learning |
| abstract | The present invention provides a method and device for counting aerial photographed highway vehicles based on deep learning, including acquiring aerial photographed road vehicle sample images under various imaging conditions, preprocessing the aerial photographed road vehicle sample images, and constructing a deep neural network model, A deep neural network model constructed by training the preprocessed aerial photographed road vehicle sample images; using the deep neural network model to detect the aerial photographed road vehicle images to be detected, and outputting the detected road and vehicle object recognition probability, object location, and object area segmentation ; According to the identified road and vehicle results, count the vehicles located in the road area, and output the count result. By sharing the deep image feature extraction network, the computing resources are effectively utilized, the running time of model training and road and vehicle detection is saved, and the accuracy and detection rate are greatly improved. The invention is applied to the application fields of traffic data collection, traffic flow monitoring, image data processing and image analysis. |
| priorityDate | 2018-09-11-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 25.