Abstract
In order to improve the effect of real-time defect recognition in steel plate online production, this paper studies the method of steel plate defect recognition based on the deep neural network algorithm based on space-time constraints. Moreover, this paper improves the space-time constraint algorithm, optimizes the encryption structure of the traditional ABE scheme, and obtains a neural network feature recognition method based on space-time constraints. In order to process the massive image data stream generated instantaneously and ensure the real-time performance, accuracy, and stability of the detection system, this paper constructs a distributed parallel computing system structure based on the client/server (CC/S) model to obtain an intelligent recognition system. Through experimental research, it can be seen that the deep neural network recognition system based on space-time constraints proposed in this paper has a good effect in the recognition of steel plate defects.
Highlights
In the past few decades, steel plate rolling production technology and product quality have achieved rapid development
The surface defects of the steel plate affect the deep pressure of the processing, the coating effect of the final product, the electromagnetic characteristics, and the aesthetics. erefore, whether it is a steel plate manufacturer or a steel plate user, it is necessary to attach great importance to the surface quality inspection of the steel plate [2]
Based on the above analysis, this paper uses a deep neural network algorithm based on space-time constraints to build an intelligent system that can be used for steel plate defect recognition and improve the intelligent detection effect of steel plate defects
Summary
In the past few decades, steel plate rolling production technology and product quality have achieved rapid development. Erefore, whether it is a steel plate manufacturer or a steel plate user, it is necessary to attach great importance to the surface quality inspection of the steel plate [2]. Whether it is automobile production, shipbuilding, machinery manufacturing, or chemical, aerospace, and other industries, steel is used as raw material for processing and production [3]. In the actual production environment, the aging of rolling equipment, different processing techniques, and differences in raw materials may affect the quality of the steel plate, resulting in the appearance of various types of defects such as cracks, scars, and scratches on the surface of the steel plate. Based on the above analysis, this paper uses a deep neural network algorithm based on space-time constraints to build an intelligent system that can be used for steel plate defect recognition and improve the intelligent detection effect of steel plate defects
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