Visual inspection principle

Visual detection refers to the task of detecting, identifying and classifying objects by analyzing and interpreting images or videos through computer vision technology. The following are the general principles of visual inspection:

Image acquisition: use cameras, video cameras and other equipment to acquire images or videos of the target.

Image preprocessing: preprocess the collected image, including denoising, adjusting brightness and contrast, image enhancement and other operations to improve the effect of subsequent processing.

Feature extraction: according to the specific task requirements, extract the information related to the target features from the image. Commonly used features include edges, colors, textures, shapes, etc. Feature extraction can use classical computer vision algorithms (such as SIFT, HOG, etc. ) or deep learning methods (such as convolutional neural networks).

Target detection and recognition: The extracted features are further processed for target detection and recognition. The task of target detection is to determine the position and bounding box of the target in the image, and the task of recognition is to determine the category of the target. This can be achieved by applying machine learning algorithms (such as support vector machines and random forests). ) or deep learning methods.

Decision and output: according to the results of detection and identification, make decisions and output. This can be target classification, location marking, etc.

It should be noted that the specific implementation of visual inspection may be different due to different application fields and tasks, and may also involve other more complicated steps and technologies, such as target tracking and attitude estimation.