Convolutional Neural Networks (CNNs) excel at handling spatial (image) data. Their architecture is primarily divided into two main parts: "feature extraction" and "classification."

When an image is input into the system, it undergoes feature extraction. This process involves multiple layers of "convolution," "ReLU activation," and "pooling," continuously refining the features of the image. The extracted features are then "flattened" to form an input, which is passed through a typical neural network before reaching the output layer to predict the classification results.

The detection system i

Based on the signal source, the labeling action is performed. In conjunction with the Jyejiang AOI system, major defects under monitoring are labeled at the corresponding positions (using special non-residue adhesive materials). This facilitates tracking anomalies by the production and quality assurance units, aiding in the identification and resolution of issues.