| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_c45d29369485a6fa3e58f067700e50b1 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20221
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30204 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-24
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T5-50
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0004
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-253
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-10 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T5-50
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-10 |
| filingDate |
2021-09-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_eeb8e9e05e824bb3fb3750d93c56ea88
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_b331a610824f60e29bba7f0ef86697fc
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e566c0f68633bc7ffac8c1849dad68fb
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_54199cfcf86c3a6358cafc1f1b0dff2c |
| publicationDate |
2021-12-03-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
CN-113744248-A |
| titleOfInvention |
A power line detection method based on deep learning |
| abstract |
The invention provides a power line detection method based on deep learning, which includes the following steps: collecting aerial images of power transmission line inspection; expanding the number of training samples through data enhancement; labeling the power lines in the images to generate a power line segmentation data set; and dividing the data set into Training set and test set; establish a U-net-based power line semantic segmentation network model; input the training set images into the semantic segmentation network model, perform network settings, repeat iterative training, and obtain a trained power line semantic segmentation network model; Input to the trained power line semantic segmentation network model for performance testing, and output power line detection results. The invention can automatically segment the power line from the aerial image, has faster detection speed and higher detection accuracy, can effectively avoid the false detection and missed detection of the power line, and is very useful for realizing the automatic obstacle avoidance of the unmanned aerial vehicle and ensuring no The safety of man-machine low-altitude flight is of great significance. |
| priorityDate |
2021-09-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |