Image recognition has long been a research-oriented field with relatively few industrial applications, creating a significant gap between academia and industry. Researchers often lack understanding of the technology's practical applications, their scenarios being largely idealistic and impractical, leaving them with limited opportunities. Industry, on the other hand, has application scenarios but lacks knowledge of image technology and doesn't readily consider using image algorithms to solve problems. However, if a complementary real-world case were to emerge, could it resonate with both academics and industry professionals?
In industry, pointer-type instruments have long been widely used in power, chemical, and automation sectors due to their simple structure and ease of operation. However, in practical applications, manual reading is still required, and certain special working environments can cause reading errors, hindering the advancement of industrial informatization. Therefore, to improve reading accuracy and work efficiency, an automatic reading recognition system for pointer-type instruments is necessary. With the development and widespread application of image processing technology, combining image processing technology with automatic reading of pointer-type instruments has become a research hotspot, and typically, those who excel in this area are primarily non-researchers.
However, for engineers in industry, image recognition technology has always been a weakness when it comes to reading pointer data from industrial instruments . If they choose to research and develop it themselves, they lack both talent and technology, and it would require significant financial and time costs. Therefore, leveraging external resources and collaborating with professionals in the field of image recognition is undoubtedly a wise choice. Fortunately, our platform brings together a group of researchers specializing in image recognition who are eager to collaborate with industry stakeholders.
To help engineers in the industrial field understand how to accurately read instrument data using image recognition principles, we are providing a system solution for automatic identification and calibration of industrial instruments. This solution, provided by an image recognition service provider on the KuaiBao platform, not only solves the problems of cumbersome data reading and reading errors in industrial instruments but also improves the work efficiency of field personnel. Furthermore, this system can simultaneously identify multiple industrial instruments and can also identify and calibrate high-precision instruments.
The entire process of image recognition for industrial instruments involves: employing specialized methods to filter the effective area and locate the instrument's pointer; calculating the angle between the line connecting the center of the dial to the pointer's rotation center and the 0-degree mark on the sub-dial, and further identifying and interpreting the pointer reading based on features such as the angle between the 0-degree mark on the sub-dial and the line segment the pointer is pointing; and then, the pointer reading is determined. Experimental results show that this positioning and recognition algorithm is computationally simple, has high accuracy, and overcomes the influence of random dial tilt on the reading recognition algorithm.
The flowchart is as follows:
pointer data collected by the system
Data read automatically by the system
