1 Introduction
Machine vision utilizes photoelectric imaging systems to acquire images of controlled targets, which are then digitally processed by a computer or dedicated image processing module. Based on information such as pixel distribution, brightness, and color, it identifies dimensions, shapes, and colors. This combines the speed and repeatability of computers with the high intelligence and abstraction capabilities of human vision, significantly improving the flexibility and automation of production.
2 Machine Vision Empty Bottle Detection System
The attached diagram illustrates the application of a PC-based machine vision system in an empty bottle inspection system on a beer production line. The system mainly consists of six parts: a camera, lens, light source, image acquisition card, PC platform, and control unit. These parts work together to ultimately complete the quality inspection and rejection of beer bottles. The following diagram, using an example, introduces the function and selection of each component.
2.1 Camera
When choosing a camera, the following aspects should be considered:
(1) Camera scanning method: Cameras can be divided into area scanning cameras and line scanning cameras according to their scanning method.
A line scan camera is a camera that scans an object line by line. Line scan cameras can be further divided into interlaced scanning and progressive scanning. Line scan cameras are suitable for the following situations: one-dimensional measurement of a stationary object; the object being in motion; processing edge images of a rotatable cylinder; and situations requiring high-resolution images of the object while considering price factors. The characteristics of line scan cameras are smooth motion, high speed tracking accuracy, and high light intensity requirements. Currently, the resolution of line scan cameras has reached several thousand lines, and the detection rate has reached 60 frames per second or even higher.
Area scan cameras can only capture one image at a time. Due to their inherent limitations, area scan cameras are unsuitable for continuous, high-precision detection of dynamic targets without omissions. However, based on their working principle, by employing the following technologies: using frame-transfer or line-transfer CCDs; using high-speed shutters (electronic shutters); using single-field technology; and using high-frequency light sources, it is entirely possible to acquire dynamic images in real time, fully meeting the requirements of industrial online inspection.
(2) Camera Color: Industrial cameras can be divided into black-and-white and color cameras according to color. Among them, black-and-white cameras have higher resolution and faster data acquisition speed than color cameras. With the continuous development of camera manufacturing technology, color cameras are now being used more and more. This is because in the past, color camera systems consisted of three cameras, which corresponded to the r (red), g (green), and b (blue) wavelengths respectively, while now single CCD color cameras have emerged. Color cameras can provide stronger observation and differentiation capabilities, and therefore play an important role in medicine, biology, and some industrial process control.
(3) Camera output interface type: The camera output interface types include RS422, RS644, USB, IEEE1394 and CameraLink, etc. When selecting an image processing card, you should pay attention to whether it supports the output type of the selected camera.
2.2 Lens
The main parameters of a lens include: image sensor size, CCD sensor size, focal length, field of view, object distance, depth of field, and angle of view. The following factors should be considered when selecting a lens:
(1) Whether the lens's imaging surface matches the CCD camera being used. The imaging surface is related to the lens's design and manufacturing. Ideally, the larger the imaging surface, the better. However, some manufacturers' lenses have smaller imaging surfaces due to design or manufacturing limitations that do not meet technical requirements.
(2) Determine the focal length, object distance, and field of view of the lens (this is mainly determined based on the actual working or installation environment). The relationship between these parameters is: the smaller the focal length, the larger the angle of view; the shorter the minimum object distance, the larger the field of view. Taking the three most commonly used lenses (50mm, 25mm, 16mm) as examples: the 50mm lens has the largest focal length, so the 50mm lens has the smallest angle of view and the smallest field of view, but the farthest minimum object distance; the 25mm lens has the next largest focal length; the 16mm lens has the smallest focal length, so the 16mm lens has the largest angle of view and the largest field of view, and the closest minimum object distance.
2.3 Light Source
Light source is a crucial factor affecting the input of machine vision systems, as it directly impacts the quality of input data and accounts for at least 30% of the application's effectiveness. Due to the vast differences in the color, material, refractive index, and other characteristics of the objects being inspected, it is essential to select the appropriate lighting device for each specific application to achieve optimal results. Light sources can be categorized by their illumination method, including backlighting, front lighting, structured light, and stroboscopic lighting. Backlighting places the object between the light source and the camera, offering the advantage of high-contrast images. Front lighting places the light source and camera on the same side of the object, facilitating installation. Structured light illumination projects gratings or line light sources onto the object, demodulating its 3D information based on the resulting distortions. Stroboscopic lighting illuminates the object with high-frequency light pulses; the camera must be synchronized with the light source for effective image capture of fast-moving objects. Light sources include halogen lamps, fluorescent lamps, and LEDs; a comparison of their main performance characteristics is shown in the attached table.
While the light source can be selected according to requirements during the design process, in most cases, choosing an LED light source is a trend.
2.4 Image Acquisition Card
An image acquisition card acts as a bridge between the camera and the computer for transmitting video signals. Currently, most cameras still output analog signals, and the image acquisition card converts various analog video signals into digital signals via A/D conversion, which are then sent to the computer for processing, storage, and transmission. The following aspects should be considered when selecting an image acquisition card:
(1) Video input format and data transfer rate: Most cameras use RS422 or EIA644 as the output signal format, so the image acquisition card needs to support the output signal format used by the camera in the system. For flexibility, it is better to support both formats. When the camera captures high-resolution images at a high speed, it will generate a high output rate. At this time, the camera usually uses multiple signals to output simultaneously, and the image acquisition card must be able to support multiple inputs and the camera's output rate.
(2) Data throughput: When the signal input rate of the image acquisition card is high, the bandwidth between the image acquisition card and the image processing system needs to be considered. When using a PC, the image acquisition card uses a PCI interface. The theoretical peak bandwidth of the PCI interface is 132 MB/s. However, in actual use, the average data transfer rate of the PCI interface on most computers is 50-90 MB/s, which may not meet the transmission needs at instantaneous high transfer rates. To avoid data loss due to conflicts with other PCI devices, the image acquisition card should have a data buffer. Under normal circumstances, 2 MB of onboard memory can meet most task requirements.
(3) Digital I/O Control: Input/output control is crucial in machine vision systems. The camera's shooting time is often determined by the processing requirements. If a reconfigurable camera is used, a reset signal needs to be generated. In some systems, a pixel clock generator is required to set the shooting frame rate. External synchronization refers to using the same synchronization signal between different video devices to ensure video signal synchronization. This ensures that the video signals output by different devices have the same frame start and end times. To achieve external synchronization, a composite synchronization signal or composite video signal needs to be input to the camera. If the image acquisition card already has digital I/O functionality and can generate the gating, triggering, and other electronic signals required by the camera and other electronic devices, it is very useful for the system; otherwise, a separate digital I/O card will be needed.
2.5 PC platform
In this system, the PC platform receives images output from the image acquisition card, which are then preprocessed, analyzed, and identified by the image processing software to determine the quality of the empty bottles. Finally, the results are sent to the PLC. Since both the image acquisition card and the image processing software consume significant system resources, a high-performance industrial PC should be selected as the PC platform to ensure fast and stable system operation.
2.6 Control Unit
This system uses a PLC as the underlying controller. It connects to photoelectric sensors, encoders, transmitters, and an image acquisition subsystem via I/O ports. The image acquisition subsystem controls the CCD camera's shooting and directly controls the transmitter's operation. Simultaneously, the PLC connects to an industrial computer via a RS-485 bus to receive control information and system parameters from the computer.
During system operation, the PLC is responsible for promptly notifying the image acquisition subsystem to activate the CCD camera and capture images of empty bottles at the shooting position. To achieve this, a photoelectric sensor is needed to detect the bottle's location. The system uses a reflective photoelectric sensor, which outputs a trigger signal when it does not receive a beam of light reflected from a reflector. The photoelectric sensor is installed near the CCD camera's shooting position, and its output is connected to the PLC's I/O input. When no empty bottle is passing by, the photoelectric sensor receives the reflected beam and outputs no signal. However, when an empty bottle passes by, the photoelectric sensor cannot receive the reflected beam and thus outputs a trigger signal. Upon receiving this signal from the input, the PLC determines that the empty bottle has reached the shooting position and outputs a start signal from the I/O output to the image acquisition system, activating the CCD camera. The camera then promptly captures an image of the detected empty bottle.
After the acquired image information is analyzed and processed by a dedicated information processing module, a conclusion is drawn regarding whether the empty bottle is of acceptable quality. If it is unacceptable, the main industrial control computer will send a control command via the 485 bus, requesting the PLC to control the ejector to eject the empty bottle. Upon receiving the ejection command, the PLC needs to calibrate the unacceptable empty bottle and track its position. When the unacceptable empty bottle reaches the ejector's position, the ejector is activated to eject the unacceptable empty bottle. To determine the ejection position, an encoder is connected to the motor driving the conveyor belt. When the motor rotates, the encoder emits pulses accordingly. By counting the number of pulses, the distance traveled by the conveyor belt can be determined. In this way, if the distance the unacceptable empty bottle travels to reach the ejection position can be measured, it can be accurately ejected. The encoder's pulse output can be connected to the PLC's I/O input port beforehand. Then, an empty bottle is placed on the conveyor belt, allowing it to pass through the detection position and the ejection position sequentially. The PLC uses a counter to record the number of pulses during this process; this value corresponds to the distance between the detection position and the ejection position.
3. Visual processing software
Vision processing software is a crucial component of PC-based machine vision systems. It primarily analyzes, processes, and recognizes images to identify specific target features. Developing vision processing software is highly complex; writing it from scratch often requires a lengthy development cycle, and self-written software may struggle to meet requirements in terms of speed and stability. To meet the needs of system integrators and end-users, image acquisition card manufacturers have developed corresponding image processing software packages for their products. This allows developers to focus on application-level software development, using these packages for secondary development and saving development costs. Therefore, when selecting a machine vision system, the image acquisition card should be chosen based on the system's required functions and the functionality of the software packages provided by the image acquisition card manufacturer. The software packages provided by image acquisition card manufacturers should possess the following functions:
Edge detection function: Edge detection is one of the most basic and commonly used tools in image processing. By detecting edges, the target in the captured image can be distinguished from the background, reducing the number of pixels to be processed and improving the software processing speed.
Target positioning function: When the empty bottles being tested pass through the camera's shooting area at high speed on the production line, due to the instability of the production line and errors in shooting time, each empty bottle will appear in different areas of the captured image. The target positioning function allows the region of interest (ROI) in the processing software to change as the workpiece's position in the image changes, always remaining located at the key part of the workpiece.
Image preprocessing functions include binarization, edge sharpening, contrast adjustment, etc. With appropriate preprocessing algorithms, the target image can be highlighted, the image analysis speed can be improved and the analysis process can be simplified. These are essential functions.
Character reading function (OCR): This function is particularly important for vision systems that are mainly used for reading various characters.
Interface functionality: The software package should be able to easily interface with other software or controls and run together.
4. Conclusion
PC-based machine vision systems are characterized by high speed, high precision, and high automation. They integrate advanced sensors, computers, digital image processing, and machine vision technologies, and can be widely applied in industrial manufacturing, electronics and semiconductors, packaging, agriculture, pharmaceuticals, and beer production. They can significantly improve the automation level of existing production lines, ensure product quality, and increase production efficiency. However, research on machine vision in my country started relatively late, and currently, the market mainly relies on imports for this type of equipment. With the continuous improvement of social productivity and the increasing automation of factories, the application prospects of this technology are very broad. Only through in-depth research and exploration in both theory and practical technology can the gap with advanced foreign technologies be narrowed and a foothold in the domestic machine vision market achieved.
