ALFA software uses neural network algorithms to solve various problems of machine vision in a modular way, and truly allows artificial intelligence to enter the field of machine vision. ALFA enables every automated device to have a brain and a human-like learning ability. It can accumulate experience in continuous work. The more experience it is, the more accurate the results will be. Realizing the replacement of humans by machines in the true sense must be done. It will set off a new round of industrial transformation.
Using the management mode of the ALFA defect detection module tool and the automatic detection and classification of the ALFA classification module will make mobile phone component inspection very simple.
First, it is necessary to collect a large number of component inspection pictures as a learning sample library, including normal products and defective products, and the inspection pictures of each defective product contain at least one group. The more samples in the sample library, the better the learning effect and the more accurate the subsequent detection.
Next, the ALFA software conducts training and learning through the image sample library, and generates a reference model for detection. This learning process takes less than 10 minutes, and the specific learning depends on the hardware configuration of the computer.
Finally, ALFA can be used in actual detection.

It's a very small cell phone vibrator, smaller than a thumb, and it's hard to see what's wrong with it with the naked eye. 1. Select the hardware: First, we select the appropriate lens and light source, so that the defects on the surface can be clearly displayed. Figure 1

figure 1

2. Positioning: When each pattern is taken, the position may be offset, resulting in the offset of the part to be detected, so the detection effect will be greatly reduced. Therefore, our software performs image positioning, as shown in Figure-2 and Figure-3, we build a model, the software will automatically capture feature points and then locate them. We take as many images as possible for training, and the results are quite ideal.

figure 2

image 3

3. Defect detection: After positioning, we have determined the scope of detection. Here we first mask the irrelevant detection part, and the remaining part to be detected is used for marking. Here we choose the middle gold surface as an example. In Figure-4, we select the area of ​​interest, and then go to Figure-5 to mask the irrelevant area, and mark the defects on the gold surface. Do this step more for the pattern, then train, and then test result.

Figure 4

Figure 5

This function is very powerful. We only need to collect enough sample maps, mark these defect maps in the software, and then train them. As a result, the detection rate can reach 97%-99%. If the pattern size is large, the detection rate can be infinitely close to 100%.

4. Operation on the assembly line: After the above series of operations, we export a runtime workspace in the software, integrate the workspace during the secondary development of the software, and apply it to the assembly line for practical operations. The effect is as follows:

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