Machine vision inspection technology is an emerging inspection technique based on computer vision research. In today's rapidly advancing industrial automation, machine vision technology has found increasingly widespread application in industry and gained growing recognition and favor among users. Consequently, people have begun considering the use of CCD cameras to capture images and transmit them to computers or dedicated image processing modules. Through digital processing, size, shape, and color can be determined based on pixel distribution, brightness, and color information. This approach combines the speed and repeatability of computer processing with the high intelligence and abstraction capabilities of human vision, giving rise to the concept of machine vision inspection technology.
It is evident that its application scope is becoming increasingly widespread. Based on these categories, we can summarize five major applications within industrial automation.
1. Vision-Guided Positioning Applications
Vision-based positioning requires machine vision systems to rapidly and accurately locate target components and confirm their positions. Machine vision is employed for positioning during loading/unloading to guide robotic arms for precise grasping. In semiconductor packaging, equipment adjusts pick-up heads based on chip location data obtained through machine vision to accurately pick up chips for bonding. This represents the most fundamental application of visual positioning in industrial machine vision. Furthermore, in semiconductor manufacturing, adjusting pick-up heads based on chip location data is notoriously challenging. Machine vision resolves this issue by enabling precise chip pickup and bonding, underscoring why visual positioning is the cornerstone application in industrial machine vision.
2. Appearance Defect Detection Applications
This process detects quality issues in products on production lines and represents the area where human labor is most frequently replaced. For instance, in the pharmaceutical sector, machine vision primarily handles dimension inspection, bottle surface defect detection, bottle shoulder defect detection, and bottle mouth inspection. With the advancement of modern industrial automation, machine vision inspection is widely applied to various checks, measurements, and part identification. Examples include surface defect detection for new energy power batteries, electronic component recognition, appearance defect detection for magnetic materials, and barcode/character recognition on product packaging. These applications share the common characteristics of continuous mass production and extremely high demands for appearance quality. As economic standards rise, machine vision inspection gains increasing prominence. It enhances production capacity for qualified products while identifying and discarding substandard items early in the manufacturing process, thereby reducing waste and lowering costs.
3. High-Precision Inspection Applications
Certain products demand precision levels as fine as 0.01–0.02 micrometers or even micron-level accuracy, rendering human inspection impossible and necessitating machine-based solutions. Prime examples include burr detection in power batteries and PCB circuit board inspection. Another key application is pharmaceutical glass bottle inspection, making the medical field one of the primary domains for machine vision.
4. Image Recognition Applications
Image recognition involves processing, analyzing, and interpreting images through machine vision to identify various patterns and objects. This enables data traceability and collection, finding extensive use in new energy batteries, circuit boards, electronic components, hardware fittings, food, pharmaceuticals, and other sectors. The most iconic example is QR code recognition. QR codes and barcodes are ubiquitous in daily life. Manufacturers embed extensive product data within these compact codes for management and traceability. As machine vision image recognition expands, barcodes on diverse surfaces become effortlessly readable and detectable. This advances modernization, significantly boosts production efficiency, and progressively reduces manufacturing costs.
5. Object Sorting Applications
Within machine vision workflows, object sorting follows recognition and inspection stages. Machine vision systems process images and coordinate with robotic arms to achieve product sorting. Traditional production lines relied on manual placement of materials at designated locations before proceeding to subsequent processes. Today, automated equipment handles material distribution. Machine vision systems capture product images, analyze them, and output results. Robots then place corresponding materials into fixed positions, achieving intelligent, modern, and automated industrial production. Commonly applied in food sorting, automated parcel sorting, and cotton fiber grading, these systems save labor, increase speed, and improve product quality-essential for corporate survival in today's economic climate. Leveraging machine vision inspection technology enables businesses to maintain a competitive edge in the marketplace.