| 摘要 |
PURPOSE:To save labor for newly developing inspection algorithm by allowing a neural network(NN) to newly execute the learning processing of an object to be inspected even in the case of using a different object so that the different object can be processed only by changing the connecting state (load) of the NN. CONSTITUTION:A neuro recollecting part 500 reproduces the NN in its inside and inputs the image information of an object to be inspected to execute recollection. When the image information of high quality is used as learning data, the output of the NN may be close to its maximum. When a decision part 600 decides that a newly inputted image information is different from the image information of high quality used as the learning data more than an allowable range due to the influence of a flaw, dirt, blur, etc., the output of the NN is reduced as a low value. Thereby, the decision part 600 can decide that the image information is defective. Since learning can be attained only by applying new image information of high quality as learning data even in the case of using a different object to be inspected, labor for developing new inspection algorithm can be omitted. |
