| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_3aef3465d83177c8907cb97fb2244937 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10016
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30132
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20192
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-12
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0004
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-242
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T5-002
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-26
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-82 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-12
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-82
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-26
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T5-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-24
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-30 |
| filingDate |
2022-07-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_9c46c8ceaeb8192919115b0c421a4dfd
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4df5c7493b4a2783e82b41a1a31ab1d9
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_6c280c4c5fc6455b2207e751b2785e5f
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_6ea139b85323e61be072bf89c4510f10
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_b8946887dfad72963554b8019b14ab77 |
| publicationDate |
2022-10-18-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
CN-115205255-A |
| titleOfInvention |
Method and system for automatic grading of stone based on deep learning |
| abstract |
The invention discloses a method and system for automatic classification of stone materials based on deep learning. The method adopts a stone material picture quality assessment module, a stone material classification feature extraction module and a stone material classification data fitting module to realize the task of automatic classification of stone materials in industrial belt high-speed transportation scenarios. The stone image quality evaluation module is a residual neural network based on multi-level features, which is used to evaluate the image quality and select high-quality dynamic frames; the stone classification feature extraction module is a digital image processing based on the watershed algorithm and the concave point detection algorithm. The segmentation module is used to extract the stone classification feature information from the screened images; the stone classification data fitting module is an improved multi-layer perceptron, which is used for data fitting to correct errors for the initial stone classification feature results. The invention has higher precision and better generalization ability under different backgrounds, and can accurately detect different types of stone material transportation scenarios. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115684208-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115618282-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115618282-A |
| priorityDate |
2022-07-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |