The eyes of product appearance defects are golden, machine vision - Xiang Hongyu

1. Machine vision: Let machines have "discerning eyes"

To put it simply, machine vision is to equip machines with "eyes" (industrial cameras, lenses) and "brains" (image processing algorithms and software), so that they can "see" things like humans, and even see more carefully, more accurately, and faster.

1. Core principles:

• "See" and see: High-resolution industrial cameras clearly capture images of product surfaces with specific light sources (such as LED ring light, backlight, coaxial light, etc.). The role of the light source is crucial, highlighting defective features (e.g., scratches, pits) or hiding distractions.

• "Think" clearly: The acquired image is transferred to the computer at high speed. The vision software in the "brain" uses sophisticated image processing algorithms (such as image enhancement, filtering, segmentation) and artificial intelligence technologies (especially deep learning) to accurately analyze images.

• "Accuracy": The software compares the processing results with preset qualification criteria such as dimensional tolerances, color ranges, surface texture standards. As soon as an area outside the standard (i.e., a defect) is identified, the system immediately makes a judgment: pass or fail.

• "Move" fast: Output signals for judgment results and trigger follow-up actions: control the robotic arm to reject defective products, alarm prompts on the screen, record defect data, etc., the whole process is usually completed in milliseconds.

2. The superpower of "Golden Eyes":

• Micron-level vision: The detection accuracy can reach the micron or even sub-micron level, and it can detect tiny scratches, burrs, and printing burrs that are not detected by the human eye.

• Lightning-fast speed: Inspect hundreds or even thousands of products per minute, easily keeping up with the pace of high-speed production lines.

• Iron will: 7x24 hours continuous work, not affected by emotions, fatigue, subjective factors, stable and reliable.

• Multi-dimensional perception: Not only can visible light be seen, but also combined with infrared, ultraviolet, X-rays, etc., to detect internal defects or special material problems. 3D vision can measure object height, volume, flatness, etc.

2. The dazzling advantages and current barriers of machine vision

• Advantage:

• Quality Guard: Significantly reduce the rate of missed detection and false detection, intercept defective products, and greatly improve the consistency of product quality and brand reputation.

• Efficiency Engine: Automated inspection is much faster than manual labor, greatly improving the overall throughput and production efficiency of the production line.

• Cost Buster: In the long run, it can save a lot of labor costs (especially in areas with high salaries or harsh environments), and reduce the loss of rework, scrap, and after-sales claims caused by defective products.

• Data Goldmine: Automatically record massive defect types, locations, frequencies, and other data, providing accurate basis for production process optimization, process improvement, and equipment preventive maintenance.

• Objective and impartial: Completely based on preset criteria, eliminating the subjectivity and volatility of manual testing.

• Humanistic care: Liberate workers from tedious, eye-hurting repetitive testing tasks and move them to higher-value positions or more comfortable environments.

• Rampart:

• High initial investment: High-quality industrial cameras, lenses, light sources, processing platforms and software systems, coupled with professional integration, debugging, and maintenance costs, have a high threshold for upfront investment, especially for small and medium-sized enterprises.

• Environmental sensitivity: Environmental factors such as light changes, vibrations, dust, and oil contamination may interfere with imaging effects, requiring careful design and protection.

• Challenges of complex defects: Current technology still has difficulties in identifying extremely subtle, low-contrast, reflective material defects, or "flexible defects" that require a combination of tactile and empirical judgment (e.g., leather and fabric feel defects).

• "Data hunger" and difficulty in adjusting parameters: Deep learning-based solutions require a large number of labeled defect samples for training, and model development, training, and parameter tuning require professional talents (AI engineers, vision engineers), which is time-consuming and labor-intensive.

• Insufficient flexibility and adaptability: When the production line switches to new products or product design changes, traditional machine vision systems may need to reprogram and adjust the light source and camera position, which is less flexible than manual labor. Detection of dynamic targets or objects in non-fixed positions is also more challenging.

• Integration Complexity: Seamlessly integrating vision systems into existing automated production lines involves mechanical, electrical, software, and other aspects of cooperation, which is complex.

3. Future trends: smarter, more integrated, and more powerful

Machine vision, the "golden eye", is still evolving and will flourish in the following directions in the future:

1. AI Deep Empowerment:

• Smarter deep learning: Models will be more efficient (less reliance on massive annotation data), more robust (adapting to lighting changes, slight occlusion), and more accurate (identifying complex, obscure defects). Unsupervised/semi-supervised learning techniques will reduce data annotation costs.

• Explainability AI (XAI): Enables AI to not only make judgments but also explain the "why" of defects, enhancing decision-making transparency and trust, and assisting engineers in improving processes.

• Generative AI Applications: Utilize generative AI to create synthetic defect data for training more robust models.

2. 3D vision popularization and enhancement:

• Cost reduction and application expansion: With the reduction of the cost and the improvement of accuracy of 3D sensors (such as structured light, laser triangulation, ToF), 3D machine vision will replace 2D vision in more fields (such as weld inspection, complex assembly verification, surface defect detection) to provide more comprehensive spatial information.

• High-precision 3D inspection: The need for critical dimensional and topography measurement in microelectronics, precision machining, and other fields is driving the development of ultra-high-precision 3D vision.

3. Tighter system integration and intelligence:

• Edge computing and cloud collaboration: simple tasks are processed in real time on the device side (edge); Complex analysis, model training, and big data management are carried out in the cloud to achieve efficient collaboration.

• Deep integration with automation/robots: Vision-guided robots (VGRs) can complete the integrated tasks of grasping, positioning, assembly, and inspection more flexibly and accurately. Deeply integrated with PLC, MES and other systems to achieve closed-loop optimization of quality data-driven production.

• "Vision + Multi-sensor" fusion: Combine thermal imaging, acoustic detection, spectral analysis and other sensing technologies to form multi-dimensional perception capabilities and solve complex detection problems (such as internal defects and material composition) that cannot be covered by a single vision.

4. Ease of Use and Flexibility Revolution:

• "No-code/low-code" platform: Graphical interfaces, pre-built algorithm modules, and automation tools will allow non-professional users to quickly configure, deploy, and adapt vision applications, lowering the barrier to access.

• Adaptive Ability Enhancement: The system can automatically adapt to product variants and minor changes in the environment, reducing the need for manual intervention. AI-based self-learning systems will continuously improve performance.

5. Expanding Application Boundaries:

• From manufacturing to broader fields: Breaking through traditional industrial testing, it shines in fields such as agriculture (fruit sorting, pest and disease identification), medical (drug packaging, equipment testing), logistics (parcel sorting, damage identification), and security (intelligent monitoring).

• Micro and macro: On the one hand, it moves into the microscopic world (semiconductor nanoscale inspection) and plays a key role in the automated inspection of large facilities (bridges, aircraft fuselages, wind turbines) on the other.

epilogue

Machine vision, an increasingly refined pair of "golden eyes", has become an indispensable core force in modern manufacturing to ensure quality and improve efficiency. Although it still faces challenges in terms of cost, adaptability to complex scenarios, and ease of use, the explosive development of artificial intelligence, the continuous leap in hardware performance, and the increasing maturity of system integration are driving it to continue to push boundaries. In the future, machine vision will be smarter, more flexible, and easier to use, and will be deeply integrated with automation, big data, cloud computing and other technologies, which will not only protect product quality more accurately, but also deeply empower intelligent manufacturing, promote industrial quality inspection to a new era of unmanned and intelligent, and become a dazzling light in the wave of Industry 4.0.