As NVIDIA paints its Physical AI inspection vision, DaoAI has already put it on the line. In December 2025, NVIDIA published a technical article noting that the CNNs (convolutional neural networks) that dominated AOI for a decade have hit a development ceiling.
Structural Limitations of Traditional CNN in AOI
In the field of Automated Optical Inspection (AOI), traditional Convolutional Neural Networks (CNN) have long dominated, but they have three significant structural limitations. Firstly, there is the high data threshold. In practical applications, each defect type requires thousands of annotated images for the model to achieve good recognition results. Take the electronics manufacturing industry as an example. There are a wide variety of subtle defects on circuit boards, such as short circuits, open circuits, and component offsets. For common defect types, it may be relatively feasible to collect thousands of annotated images. However, for rare defects, due to their extremely low occurrence frequency, it is very difficult to collect enough samples. According to industry statistics, in the manufacturing process of some complex electronic products, the number of samples for rare defects may be less than 10% of that for common defects, which significantly reduces the accuracy of traditional CNN when dealing with these rare defects.
Secondly, traditional CNN has limited semantic understanding. Although the model can recognize objects in images, it cannot understand the context information and is difficult to infer the root cause of defects. For example, when detecting a circuit board, it can only identify that there is an abnormality in the position or appearance of a certain component, but cannot determine whether this abnormality is caused by production process problems, raw material quality issues, or equipment failures. This means that in actual production, even if a defect is detected, manual further analysis of the cause is required, increasing the production time and cost.
Finally, continuous retraining is also a major drawback of traditional CNN. When manufacturers switch product lines, due to the possible differences in the appearance, structure, and defect types of different products, they need to re-annotate the data and retrain the model. As product lines are continuously updated and expanded, the maintenance cost will continue to accumulate. Data shows that in some large-scale electronic manufacturing enterprises, the cost of model retraining accounts for more than 30% of the total inspection cost each year.
The New Direction of AOI Verified by NVIDIA
Facing the limitations of traditional CNN, NVIDIA has explored a new technical route. They use a pre-trained visual foundation model, first perform domain adaptation with millions of unannotated factory images, and then fine-tune it with a small amount of annotated data. This method makes full use of the information in a large amount of unannotated data and reduces the dependence on annotated data. In the PCB defect detection experiment, this method achieved remarkable results. The experimental results show that the accuracy of PCB defect detection increased from 93.84% to 98.51%. This improvement not only enhances the reliability of detection but also reduces the situations of misjudgment and missed judgment, providing stronger guarantee for production quality.
NVIDIA's experimental results show that the method of using a pre-trained visual foundation model combined with fine-tuning with a small amount of annotated data can effectively improve the detection accuracy of AOI.
DaoAI's Innovative Implementation
DaoAI takes a step further in the direction verified by NVIDIA. It integrates this technical route into hardware and provides a plug - and - play solution. Based on the Visual Geometry Group (VGG) architecture of the visual foundation model, it is trained on more than one million real SMT factory images and is specifically adapted to PCBA manufacturing. Different from traditional methods, DaoAI performs feature extraction in the feature space rather than the pixel space. This method can more effectively capture the essential features of images and improve the accuracy and efficiency of detection.
From the data of actual production line applications, the advantages of DaoAI are very obvious. The programming time is reduced by 97%, which means that manufacturers can apply the detection system to production more quickly, shortening the product launch cycle. The false alarm rate is reduced by 80%, greatly reducing the workload of manual re-inspection and improving production efficiency. The operating cost is reduced by 60%, which is a huge cost saving for manufacturers.
Manufacturers' Choices and Industry Trends
In today's continuously developing AOI technology, manufacturers face an important choice: whether to adopt a technology stack that needs to be assembled by themselves or deploy a detection system that is ready - to - use and continuously self-optimizing. Although the traditional technology stack can be customized according to their own needs, it requires a large amount of human, material, and time resources for development and maintenance. For some small and medium-sized enterprises, this may be an unbearable burden. And a plug - and - play solution like DaoAI, with its low threshold, high efficiency, and low cost, is more in line with the development needs of modern manufacturing.
From an industry trend perspective, with the continuous progress of artificial intelligence technology, AOI systems will develop towards greater intelligence, automation, and high-efficiency. Pre - trained visual foundation models and hardware-integrated solutions will become the mainstream in the future. Manufacturers should pay timely attention to these technological trends and choose a detection system suitable for themselves to improve production quality and competitiveness.
FAQ
What are the structural limitations of traditional CNN in the AOI field?
Traditional CNN has three major structural limitations in the AOI field. It has a high data threshold, requiring thousands of labeled images for each defect type. It has limited semantic understanding and can't understand context or infer root causes. Also, it needs continuous retraining, increasing maintenance costs when switching product lines.
What is the AOI technology direction verified by NVIDIA?
NVIDIA uses a pre-trained visual foundation model. It first performs domain adaptation with millions of unlabeled factory images and then fine-tunes with a small amount of labeled data. The experiment has significantly improved the PCB defect detection accuracy.
How does DaoAI implement the technology on the production line and what are the effects?
DaoAI integrates the technology into hardware. Based on the VGG architecture, after training on over 1 million real SMT factory images and domain adaptation, it reduces programming time by 97%, false alarm rate by 80%, and operating costs by 60% on the production line.
This article was generated by AI. Customer cases are simulated scenarios based on real product capabilities and figures are illustrative; see product pages for official benchmarks.