Abstract
In this paper, strategies in image preprocessing, hardware composition and detection methods are considered to improve computerized machine vision detection accuracy. First, image preprocessing and image enhancement are performed to improve the quality of the input image. Second, the hardware composition of the computer vision online inspection system is optimized by focusing on the light source selection and the performance of the image acquisition card in spatial applications. Combined with spatial application calculations, methods such as frequency domain method and Canny operator are used in order to improve the accuracy of machine vision detection. Finally, in the same test environment, the machine vision detection requires only 400MB and the detection accuracy ranges from 85.13% to 99.42%. With these comprehensive strategies, this paper provides a comprehensive and effective approach for computerized machine vision detection in spatial applications to improve detection accuracy and meet demanding application scenarios.
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