Machine vision systems replace human eyes for measurement and judgment. They can quickly acquire large amounts of information, are easily processed automatically, and are readily integrated with design and manufacturing information. Therefore, in modern production processes, machine vision systems are widely used in areas such as condition monitoring, finished product inspection, and quality control. Due to their high precision, non-contact nature, and stability, they have been widely applied in domestic and international industrial sectors, significantly improving product quality and the automation level of production lines.
Overall, China lags behind foreign countries in machine vision product research and development, currently maintaining a relatively low level. Foreign products are widely used. Current applications are mainly concentrated in areas such as inspection, positioning, and [other applications]. However, in the past two years, the demand for machine vision applications has grown most rapidly in motion control, production lines, diagnostics, testing, and CNC equipment. Research on machine vision software is also deepening. As industry applications become more numerous and demanding, current machine vision systems on the market struggle to meet industry needs, generally suffering from difficulties in development, use, and sales. These shortcomings are mainly reflected in insufficient system standardization, requiring significant development manpower for each customer, weak system reusability, poor user experience, and high maintenance costs and difficulties in later maintenance. Therefore, developing a machine vision system that requires no programming, is easy to configure, provides hardware and software configurations, and is easy to sell is essential.
This article introduces a novel, non-programmable machine vision system. Addressing the current market situation, it offers the following advantages: 1. Configurable and programmable, as simple and easy to use as a household appliance; 2. Provides a universal application platform encompassing numerous unit machine vision tools; 3. Not based on a specific smart camera or general vision system, but integrating underlying and application development; 4. Vertically integrated, combining the core system with application processes; 5. Modular design, providing aftermarket support for equipment manufacturers and end-users; 6. Simplified system sales model; 7. Optimized system cost structure, resulting in a product with a significant price advantage. The detailed design concept of this system is described below from both hardware and software perspectives.
1. Hardware Framework
This system monitors and measures equipment operation based on image analysis technology. It uses feature extraction and template comparison to compare real-time images acquired on-site with standard images from normal operating conditions. This enables it to detect anomalies, analyze and judge their effects, and trigger alarms and shutdowns. The standard template is based on the analysis of images from normal operating conditions, extracting features. The comparison only compares the features (combinations) of the template with corresponding features in the real-time on-site images to determine if any anomalies have occurred. This monitoring method cannot replace the self-diagnostic function of automated equipment, but it is highly effective in preventing operational deviations, preventing defective products, and even preventing equipment damage. The product consists of the following main parts:
1. DSP/ARM-based core processor
2. Optical lens, image sensor, front-end processing and interface (i.e., camera part)
3. FPGA acts as an accelerator for image processing and a peripheral interface logic.
4. GPIO and Serial Communication
5. Ethernet communication
Figure 1 System Hardware Framework
As shown in Figure 1, the DSP/ARM processor uses a TIOMAP4460 CPU with an ARM Cortex-A9 clock speed of 1.8GHz and a built-in DSP, which is higher than the performance level of most smart camera hardware platforms to date. The camera resolution is mainly 1280x1024, but it also supports cameras with 5 megapixels or higher. The storage supports NORFLASH and NANDFLASH, and can be expanded with SD cards. The FPGA serves as a logic expansion device for peripherals, mainly with two functions: buffering and preprocessing image data before sending it to the DSP processor for corresponding algorithm processing. In terms of communication, it supports Ethernet and serial ports, which can be easily connected to robotic arms and other devices. The I/O settings are equipped with GPIO (General Purpose Input/Output), which connects to trigger signals sent from the field (input, triggering image capture) and control signals sent to the device (output).
In addition, the system can be provided in two forms:
Visual flat panel. A hardware platform with various functional interfaces required for visual applications, featuring the following characteristics: LCD display and touch screen functionality (large screen); built-in high-performance processor (ARM architecture); camera access; I/O interfaces: RS-232, USB, 10/100/1000 Ethernet; network communication function: WiFi; encryption protection.
Figure 2 Visual tablet Vpad
Smart camera. Features include: compact size (small and lightweight, industrial-grade); on-board camera; I/O: GPIO; network connectivity: Ethernet, WiFi; encryption protection, etc. (See Figure 3).
Figure 3 Smart Camera VDSR
2. Software Technology
The ARM processor in the system is primarily responsible for resource management and logic control. It runs on the AngstromLinux system and integrates drivers for the functional modules required for machine vision. The system software development includes driver implementation for the USB camera, GPIO, LCD touchscreen, RS-232, etc., as well as porting the AngstromLinux system. AngstromLinux is a desktop distribution that runs on embedded platforms. It integrates most system tools and libraries, including QT and OpenCV libraries used for software and algorithm development. It allows for online compilation to select the required libraries, offering a high degree of customization, making it ideal for resource-constrained embedded products. The drivers for the underlying modules are implemented by modifying and porting them from the official Linux kernel.
The application-layer software development is based on the Ubuntu system, using QT for both interface and logic design. Ubuntu is a free and open-source desktop PC operating system based on Linux, which can easily configure environments similar to those on embedded platforms, such as QT and OpenCV. The system's software design goals are no programming required, simple configuration, and modularity. Therefore, all potentially used vision tools are integrated into a single integrated software platform, vdStudio, including commonly used tools for positioning, measurement, detection, and calibration. Combined with a graphical user interface, users can easily select and configure each tool, and the interface is designed based on user habits, making it simple, easy to learn, and easy to use. Furthermore, the system supports offline operation, achieving one-time configuration and permanent operation. (See Figure 4.)
Figure 4 User Graphical Interface
3. Algorithm Research
Algorithms are the core of machine vision systems, contributing the most to visual functionality and proving most critically compared to other parts of the system. In terms of market share, standard machine vision systems based on core software algorithms account for almost half of the entire machine vision industry, highlighting the importance of algorithms and software. If the algorithm is poor, visual functions cannot be achieved, or while they may be achieved, their performance will be uncompetitive. Even with a good algorithm, poor software development can directly impact the optimization and performance of the vision system. Algorithms and software systems are the most important assets for a vision company that produces standard systems. In market competition, the competitiveness of algorithms often plays a crucial role. Besides existing resources, acquiring more algorithmic software resources is also a key factor in the success of this project. In addition to developing our own algorithms in specific niche areas, collaborating and sharing revenue with internationally renowned software and algorithms is an important path to improving our products.
The system software design employs several excellent design patterns, allowing for flexible selection of algorithms during practical use. These include the independently developed VD300SDK (Software Development Kit), which includes function libraries for CG algorithms, datum plane correction, image stitching, and scanning control; an algorithm SDK based on OpenCV, including tools for template matching, measurement, and blob detection; and the general-purpose machine vision function library IPT (Image Processing Toolset).
4. Conclusion
This system allows for direct use of visual appliances without ASP integration or programming. Like household appliances, it's easy to operate by simply reading the instruction manual. There's no technical integration layer between the manufacturer and the user, saving costs that can be passed on to the user, lowering the product price. Users receive the functionality they need without needing extensive technical details. Salespeople only need to identify their target audience, explain the product's features, and highlight its competitive advantages; even ordinary salespeople without strong technical backgrounds can handle the job.
