Machine vision inspection system selection_Machine vision automatic inspection technology

Machine vision inspection system selection involves a number of factors, including application scenarios, needs, budget and so on. The following are common machine vision automatic inspection techniques:

1. Target detection: target detection is a common machine vision automatic inspection technique used to identify and locate specific objects or targets in images or videos.

2. Behavioral recognition: Behavioral recognition is a machine vision automatic detection technique for video data to identify and analyze the behavior of people or objects. Common behavior recognition methods include traditional machine learning-based methods (e.g., Hidden Markov Models, Conditional Random Fields, etc.) and deep learning-based methods (e.g., LSTM, 3D CNN, etc.).

3. Image segmentation: image segmentation is a machine vision automatic detection technique that divides an image into several different regions or objects. Common image segmentation methods include those based on traditional machine learning (e.g., based on edge, region growing, etc.) and those based on deep learning

4. Feature extraction: feature extraction is an important part of machine vision automatic detection technology, which is used to extract representative features from an image or a video for the subsequent classification, recognition and other tasks. Common feature extraction methods include traditional hand-designed features (e.g., SIFT, HOG, etc.) and deep learning-based methods (e.g., convolutional neural networks, etc.).

When selecting a model, it is necessary to choose a suitable automatic machine vision inspection technology based on specific application scenarios and needs, and to consider relevant algorithmic performance, real-time, reliability and other factors. At the same time, it is also necessary to select the appropriate hardware platform and software tools according to the budget.