Advantages of Machine Vision Technology Machine vision systems can quickly acquire large amounts of information, are easy to automate, and are easily integrated with design and processing control information. Therefore, in modern automated production processes, machine vision systems are widely used in areas such as condition monitoring, finished product inspection, and quality control. The key feature of machine vision systems is improved production flexibility and automation. In hazardous working environments unsuitable for manual labor or where human vision is insufficient, machine vision is often used to replace human vision. Furthermore, in large-scale industrial production, manual inspection of product quality is inefficient and inaccurate; machine vision inspection methods can significantly improve production efficiency and automation. Moreover, machine vision facilitates information integration and is a fundamental technology for computer-integrated manufacturing. In short, with the maturation and development of machine vision technology, it is expected to be increasingly widely used in modern and future manufacturing enterprises. Types of Machine Vision Industrial Inspection Systems Machine vision industrial inspection systems, based on their inspection nature and application scope, are divided into two main categories: quantitative and qualitative inspection, each further subdivided. Machine vision is actively used in various applications of industrial online inspection, such as visual inspection of printed circuit boards, automatic flaw detection of steel plate surfaces, parallelism and perpendicularity measurement of large workpieces, container volume or impurity detection, automatic identification and classification of mechanical parts, and geometric dimension measurement. Furthermore, machine vision systems can effectively address many situations where other methods are difficult to use. The application of machine vision is increasingly replacing humans in many tasks, which undoubtedly greatly improves the level of production automation and the intelligence level of inspection systems. Examples of Machine Vision Applications in Quality Inspection Machine vision systems have been widely used in various aspects of quality inspection. For example, a large workpiece parallelism and perpendicularity measuring instrument using a laser scanning and CCD detection system uses a stable collimated laser beam as the measurement baseline, coupled with a rotating axis system, and a rotating pentagonal prism to scan mutually parallel or perpendicular reference planes, which are then compared with the faces of the large workpiece being measured. When machining or assembling large workpieces, this instrument can be used to measure the parallelism and perpendicularity between surfaces. A dynamic detection system for online measurement of the geometric parameters of hot-rolled rebar is achieved by using stroboscopic light as the illumination source and surface-mounted and line-mounted CCDs as detection devices for the outline dimensions of rebar. Vision technology monitors bearing load and temperature changes in real time, eliminating the dangers of overload and overheating. It transforms the traditional passive measurement method of ensuring processing quality and safe operation by measuring the surface of ball bearings into active monitoring. Using microwaves as a signal source, square waves with different wave frequencies are emitted by the microwave generator to measure cracks on the metal surface; the higher the frequency of the microwave wave, the narrower the detectable crack. In short, there are many similar practical systems, which will not be listed here. Below, we will introduce three practical machine vision systems in more detail. The EQ140-II automotive instrument panel assembly intelligent integrated testing system is based on machine vision. The instrument panel is a product manufactured by a Chinese automotive company, equipped with a speedometer, odometer, water temperature gauge, fuel gauge, ammeter, and warning lights. Its production volume is large, and a final quality inspection is required before leaving the factory. The inspection items include: checking the indicating error of five instrument pointers, such as the speedometer; and checking whether 24 signal alarm lights and several lighting lights (9 lights in total) are damaged or missing. Generally, manual visual inspection is used, which has large errors, poor reliability, and cannot meet the needs of automated production. The intelligent integrated testing system based on machine vision changes this situation, realizing intelligent, fully automatic, high-precision, and rapid quality inspection of the instrument panel assembly, overcoming various errors caused by manual inspection, and greatly improving inspection efficiency. The entire system is divided into four parts: an integrated multi-channel standard signal source providing analog signal sources for the instrument panel; a dual-coordinate CNC system with image information feedback positioning; a camera image acquisition system; and a master-slave parallel processing system. Automatic Scratch Control System for Metal Plate Surfaces The surface quality of metal plates, such as large power transformer coils, flat wires, and radios, has very high requirements. However, the original inspection methods using manual visual inspection or dial indicators with control probes are not only easily affected by subjective factors but may also introduce new scratches to the tested surface. The automatic flaw detection system for metal plate surfaces utilizes machine vision technology to automatically inspect metal surface defects. It performs high-speed and accurate detection during production, and the non-angle-based measurement method avoids the possibility of creating new scratches. Its working principle is shown in Figure 8-6. In this system, a laser is used as the light source. A pinhole filter removes stray light around the laser beam, and a beam expander and collimator make the laser beam parallel and uniformly illuminate the inspected metal plate surface at a 45-degree incident angle. The metal plate is placed on an inspection table. The inspection table can move in the X, Y, and Z directions. The camera uses a TCD142D type 2048-line CCD, and the lens is a standard camera lens. The CCD interface circuit uses a microcontroller system. The host PC mainly performs image preprocessing and defect classification or scratch depth calculation, and displays the detected defect or scratch images on the monitor. The CCD interface circuit and the PC communicate bidirectionally via an RS-232 port, combined with asynchronous A/D conversion, forming an interactive data acquisition and processing system. This system primarily utilizes the self-scanning characteristics of a linear CCD combined with the X-axis movement of the inspected steel plate to obtain three-dimensional image information of the metal plate surface. Automotive Body Inspection System The 100% online inspection of the dimensional accuracy of the 800 series automotive body contours by the British ROVER automotive company is a typical example of machine vision systems used in industrial inspection. The system consists of 62 measurement units, each including a laser and a CCD camera, used to inspect 288 measurement points on the car body shell. The car body is placed under the measurement frame, and its precise position is calibrated by software. The calibration of the measurement units affects the inspection accuracy and is therefore given special attention. Each laser/camera unit is calibrated offline. There is also a calibration device calibrated offline using a coordinate measuring machine, which can perform online calibration of the camera top. The inspection system inspects one car body every 40 seconds, covering three types of car bodies. The system compares the inspection results with the acceptable dimensions drawn from a CAD model by a human, with a measurement accuracy of ±0.1mm. ROVER's quality control personnel use this system to determine the dimensional consistency of key components, such as the overall body shape, doors, and windows. Practice has proven the system's success, and it will be used for body inspection of other ROVER models. Banknote Printing Quality Inspection System This system utilizes image processing technology to compare and analyze over 20 features (serial numbers, Braille, color, patterns, etc.) of banknotes on the production line to detect banknote quality, replacing traditional methods of human visual inspection. Intelligent Traffic Management System By placing cameras at key traffic intersections, when a vehicle violates traffic rules (such as running a red light), the camera captures the vehicle's license plate and transmits the image to the central management system. The system uses image processing technology to analyze the captured image, extract the license plate number, and store it in a database for management retrieval. Metallographic Analysis The metallographic image analysis system can accurately and objectively analyze the matrix structure, impurity content, and composition of metals or other materials, providing a reliable basis for product quality assessment. Medical Image Analysis Automatic blood cell classification and counting, chromosome analysis, cancer cell identification, etc. Bottled beer production line inspection system This system can detect whether the beer meets the standard volume and whether the beer label is intact, among other things.