Patent ID: 11966847
Assignee: DONGHUA UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 2:
3. The method of claim 1, wherein
step 3 comprises the following sub-steps:
step 301) performing a genetic encoding on the candidate convolution kernel subset obtained in step 2; according to combination characteristics of the candidate convolution kernel subset, and screening the candidate convolution kernel subset by using a 0-1 encoding method, wherein a gene length is a length of the candidate convolution kernel subset; when a gene is 1, a candidate convolution kernel is deleted, and when the gene is 0, the candidate convolution kernel is retained;
step 302) calculating a classification accuracy of the initial product surface defect detection model after deleting the convolution kernels according to the candidate convolution kernel subset after the genetic encoding, and at a same time, using the classification accuracy as a value of a fitness function in the genetic algorithm to perform a selection operation; and
step 303) performing the selection operation by using a two-point crossover and a mutation operation; continuously optimizing by using the genetic algorithm to find redundant and low-impact convolution circuits in the initial product surface defect detection model; and pruning the redundant and low-impact convolution circuits to reduce a calculation complexity of the initial product surface defect detection model.