How to detect product defects by visual inspection?

The visual inspection method for product defects mainly includes the following steps:

1. Collect product images: use a camera or scanner to collect product surface images to obtain product texture, color, shape and other characteristic information.

2. Denoising and image enhancement: preprocess the collected image to remove noise, enhance contrast and smooth the image, thus improving image quality and recognition accuracy.

3. Feature extraction: Using image processing technology to extract product feature information, such as the size, shape, color and location of defects.

4. Pattern recognition and classification: using machine learning or deep learning technology to classify and identify the feature information of products. By training the model, we can identify different types of defects, such as scratches, burrs, gaps, deformation and so on.

5. Result output: output the detected defects in a certain way, such as the size, shape, location and other information, as well as the statistical analysis report of the detection results.

The visual inspection method of product defects can quickly and accurately detect product surface defects and improve product quality and production efficiency. At the same time, it can also realize automatic and intelligent detection, and reduce the cost and misjudgment rate of manual detection. After combining AI algorithm with machine vision system, Hua fused the texture information of the image with the morphological information of 3D image, combined with deep learning technology, and completed the surface quality inspection, breaking the traditional machine vision barrier. In practical production and application, it shows good adaptability to different positions, lighting and complex environment, and has high fine recognition rate, high detection rate and correct rate, which greatly increases the manufacturing flexibility.