http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111274935-A

Outgoing Links

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assignee http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_a455ccb58b99c6e12e24ce944f5a2766
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filingDate 2020-01-19-04:00^^<http://www.w3.org/2001/XMLSchema#date>
inventor http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_b4cb0e8cd81ceff2332a1a9960023dc0
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http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_475d9b174e6f0b8fdccfd8f1ce5b937f
publicationDate 2020-06-12-04:00^^<http://www.w3.org/2001/XMLSchema#date>
publicationNumber CN-111274935-A
titleOfInvention A deep learning-based water ecological information identification method and system
abstract The invention relates to the field of environmental technology, in particular to a deep learning-based water ecological information identification method. The process includes collecting environmental information to obtain a first environmental image; extracting information from the first environmental image to obtain a second environmental image; The deep learning training is obtained, the multiple sets of data include the first type of data and the second type of data, and each set of data in the first type of data includes: an image including the target object and a label that identifies the image as including the target object; the second type of data Each set of data in includes: an image that does not include a target and a label that identifies that the image does not include a target; obtains output information of the model, and outputs the target information if the second environment image has a target . Secondly, the present invention also provides a deep learning-based water ecological information identification system. The invention can quickly and accurately identify the target object information.
priorityDate 2020-01-19-04:00^^<http://www.w3.org/2001/XMLSchema#date>
type http://data.epo.org/linked-data/def/patent/Publication

Incoming Links

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Total number of triples: 23.