Point-by-point feature extraction of artificial intelligence images based on the Internet of Things

Abstract

In the context of the rapid development of artificial intelligence in the Internet of Things, the establishment of the Internet of Things can promote rapid progress in the field of artificial intelligence. Traditional image detection methods use wavelet energy algorithms to divide background and edge noise, with poor resolution and low image detection accuracy. A series of problems such as slow detection speed and lack of image depth analysis exists. Aiming at the disadvantages of traditional methods, this study proposes the design of an artificial intelligence image detection system based on the Internet of Things, and uses intelligent artificial pixel feature collection technology to extract point-by-point feature of the image. This paper introducing artificial intelligence learning algorithms to the wheel detection in the workshop under the Internet of Things system, which can not only solve the problem of poor feature anti-interference and poor robustness in the traditional method, but also has important significance for the secondary development of the wheel detection system. The neural network can be used to classify wheel images while integrating other detection requirements, such as wheel defect detection and wheel number identification. The rich data resources and processing capabilities of the Internet of Things to perform feature analysis and feedback on the collected image pixels are utilized. The artificial intelligence of image synthesis module performs image conversion processing on the signal processes the feedback signal. The analysis results can complete image detection and complete artificial intelligence images. Through simulation experiments, it is proved that the design of artificial intelligence image detection system based on the Internet of Things has the advantages of high image detection rate, high recognition accuracy, stable operation, and efficient processing. The design idea has good application value.