The process of machine vision inspection usually includes

The process of machine vision inspection usually includes:

1. Image acquisition:

Camera or sensor: use a camera or other sensor to obtain the image or video data of the target object or scene.

Data collection and preprocessing: preprocess the collected image data, including denoising, enhancement, cutting and other operations to improve the accuracy and efficiency of subsequent processing.

2. Image processing and feature extraction:

Feature extraction: using image processing technology to extract image features, such as edges, colors, textures, etc.

Feature selection: Select the most relevant and effective features according to the specific detection tasks.

Image segmentation: divide the image into different regions to better identify and analyze the target object.

3. Object identification and detection:

Target detection: use machine learning algorithm or deep learning model to identify and detect objects. This may include information such as the location, size and category of the object.

Classification and recognition: classify and recognize the recognized objects and determine their categories or types.

4. Target tracking and positioning:

Target tracking: if it is a video stream, it may be necessary to track the motion trajectory and position change of the object in real time.

Positioning: Determine the precise position and spatial relationship of the object in the image.

5. Result analysis and output:

Result output: output the results of identification, detection or tracking in the form of visual data, reports or images.

Post-processing: Post-processing the output results, which may include filtering, fusion, correction and other operations to improve the accuracy and reliability of the results.

6. Feedback and optimization:

Model optimization: Optimize and adjust the model according to the actual application to improve the accuracy and stability of detection.

Data feedback: According to the feedback of practical application, it may be necessary to adjust the parameters of data set, algorithm or model to continuously improve the detection effect and performance.

Machine vision detection is a complex process, involving knowledge and technology in many fields, including image processing, pattern recognition, machine learning and deep learning. With the continuous progress of technology and the enrichment of application scenarios, the methods and processes of machine vision inspection are constantly developing and improving.