AOI-defect-detection

Automated Optical Inspection(AOI) is a critical technique which is used in the manufacture and test of electronics printed circuit boards, PCBs and so on. AOI defect detection allows us to inspect is there any defects on electronics assemblies or in particular PCBs fastly and accurately. It is the method to ensure that the quality of product can be built correctly and without manufacturing faults.

In the training process, I use data augmentation in order to apply more data, then I use VGG16, Densenet121 and so on model to train the network. In comparison with multiple pre-trained model, the result shows that Densenet performs the best among all.

Content

• Environment
• Method
• Honor
• Notice
• Reference

Environment

• Python: 3.6.5
• Keras: 2.2.5
• Tensorflow: 1.14.0

Method

1. Here we use the data from Industrial Technology Research Institute - Aidea to classify the defect. Unzip the file, it includes:

   • train_images.zip： 2528 images.
   • test_images.zip：10142 images.
   • train.csv：two columns, ID and Label respectively.
   • test.csv：two columns, ID and Label respectively.
   • ID is for the name of the png file. Label is for the class（0: normal, 1: void, 2: horizontal defect, 3: vertical defect, 4: edge defect, 5: particle）

2. Create a folder. Put the file inside the floder. And create

   • Train_image
   • Test_image

3. Run the py file.

Honor

In private score gets 99.42500 % in accuracy.

Notice

In order not to violate the rules of the competition, the code provided here is just the example of the code. Not the exact code which is uploaded in th competition. You can change the network structure on your own.

Reference

https://aidea-web.tw/topic/a49e3f76-69c9-4a4a-bcfc-c882840b3f27

https://keras.io/applications/

https://keras.io/applications/#densenet