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Machine vision detection technology refers to the use of computer vision technology to analyze, identify, detect and track images, videos or real-time scenes through computers, so as to achieve the detection and recognition of targets. At present, machine vision inspection technology has been widely used in many application fields, such as face recognition, behavior analysis, object tracking, image retrieval, medical image analysis, etc. This article will introduce the classification of machine vision inspection technology and its application.
1. Image processing technology: Image processing technology refers to improving image quality and feature information through preprocessing, filtering, enhancement and other operations on images, so as to improve the accuracy and effect of follow-up detection tasks. Commonly used image processing techniques include image enhancement, image filtering, image segmentation, and image registration. Image enhancement technology is mainly used to improve the brightness, contrast, clarity and other aspects of the image to reduce image noise and enhance image features.
Image filtering technology is mainly used to smooth images, remove image noise, edge detection, etc. Image segmentation technology is mainly used to segment an image into multiple subregions for subsequent object detection and recognition. Image registration technology is mainly used to accurately align two or more images for image overlay, target tracking, etc.
Second, feature extraction technology: feature extraction technology refers to the extraction of features with target information and discrimination from images for target detection and recognition. Commonly used feature extraction techniques include: local binary mode, direction gradient histogram, scale invariant feature transformation, velocity robust features, etc. These feature extraction technologies can extract characteristic information such as color, texture, shape, edge, and corner point related to the target from the image for subsequent object detection and recognition. In the process of feature extraction, suitable feature extraction methods and feature descriptions can be selected according to different tasks and needs.
Third, target detection technology: target detection technology refers to the automatic detection of targets by locating and identifying targets in images or videos. Commonly used object detection techniques include: template matching, edge detection, color segmentation, binary value segmentation, region growth, connected regions, connected region markers, region contour analysis, etc. These object detection technologies can extract target-related information from the image according to the characteristics and morphology of the target, and then carry out image processing and feature extraction, and finally realize the automatic detection and recognition of the target.
Fourth, target tracking technology: Target tracking technology refers to the continuous tracking and positioning of targets in the video sequence to achieve real-time monitoring and tracking of targets. Commonly used target tracking techniques include: particle filtering, Kalman filtering, correlated off wave block matching, template matching, region matching, etc. These target tracking technologies can track and locate targets in a continuous video sequence according to their motion characteristics and apparent information, so as to realize real-time monitoring and tracking of targets.
Fifth, behavioral analysis technology. Behavioral analysis technology refers to the behavioral monitoring and analysis of targets by extracting and analyzing behavioral features of objects in images or videos. Commonly used behavior analysis techniques include: pose recognition, action recognition, behavior recognition, behavior modeling, etc. These behavior analysis technologies can analyze and identify the behavior of the target according to the motion characteristics and behavior patterns of the target, so as to realize the behavior monitoring and analysis of the target.
6. Image retrieval technology. Image retrieval technology refers to the retrieval and query of images by extracting content features and matching similarity of images. Commonly used image retrieval techniques include: color histogram matching, texture feature matching, shape feature matching, local feature matching, etc. These image retrieval technologies can retrieve and query images according to their content characteristics and similarity, so as to achieve rapid retrieval and query of images.
7. Medical image analysis technology. Medical image analysis technology refers to the detection and diagnosis of diseases and abnormalities through the analysis and processing of medical images. Commonly used medical image analysis techniques include: tumor detection, lesion identification, medical image registration, medical image segmentation, medical image registration, etc. These medical image analysis technologies can perform image processing, feature extraction and pattern recognition according to the characteristics of diseases and abnormalities in medical images, and finally realize the detection and diagnosis of diseases and abnormalities.
Machine vision detection technology, mainly including image processing technology, feature extraction technology, object detection technology, target tracking technology, behavior analysis technology, image retrieval technology and medical image analysis technology. These technologies can be flexibly combined and applied according to different application scenarios and needs, so as to achieve tasks such as detection, recognition, tracking and analysis of objects in images, videos and real-time scenes.
With the continuous development of machine vision technology, it is believed that machine vision inspection technology will be more widely used and further improved in the future.
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