http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114972895-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_7007ba2db892cc54b39fa0cebaf4e3c8 |
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F2218-12 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F2218-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/Y02A40-81 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-26 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-25 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-44 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-764 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-82 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-25 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-764 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/G06V10-44 |
| filingDate | 2022-08-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_eda461f204cf0c9e6bb01c917c5c60cd http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ec95c77ac8e0f8168a89e57e901ce00c http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a063a7cdc11dba24570fac55c3e8d2a4 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_921c2dac27527e961b51b2742d133729 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_52c139741618b7ea81a2047fb4201cc0 |
| publicationDate | 2022-08-30-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-114972895-A |
| titleOfInvention | Method and device for nondestructive testing of crayfish quality |
| abstract | The invention provides a method and device for nondestructive testing of crayfish quality, and relates to the field of hyperspectral testing. The method includes: acquiring a three-dimensional color image of each wave band of the crayfish to be tested with its abdomen facing downward; The lobster outline image is matched with the template library to determine the stylized crayfish image, and the target size of the crayfish to be inspected is obtained; according to the three-dimensional color image, the spectral features are extracted and input to the trained convolutional neural network model, and the prediction result of the content of the crayfish to be inspected is output. Determine the maturity; according to the target size and maturity of the crayfish to be inspected, screen the crayfish that meet the standards in weight, volume and maturity; wherein, the template library includes each crayfish model, multiple images of crayfish with different angles of the back profile and Correspondence of target size. The method solves the problems of inaccurate weight classification and inability to accurately distinguish maturity with naked eyes, avoids the limitations of conventional hyperspectral detection methods, and has high detection accuracy. |
| priorityDate | 2022-08-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 31.