In the industrial manufacturing and inspection field, many companies have long faced the same challenge—improving detection efficiency requires investing significant time and cost in collecting, organizing, and labeling various defect samples. However, defects often occur randomly, making it extremely difficult to obtain a comprehensive and accurate dataset.
Now, with advancements in AI technology, the traditional approach that relies on vast amounts of defect samples is gradually being disrupted.
Here, we’d like to introduce a tool that completely revolutionizes conventional thinking—the DaoAI AOI System software. Unlike traditional models that depend on defect data for training, DaoAI AOI can detect defects using only normal samples. This means companies no longer need to worry about the challenges of collecting hard-to-find "abnormal data."
Train with Only Normal Samples to Detect Unknown Defects
Traditional defect detection methods typically require collecting a large number of "abnormal" samples to train models on what constitutes a "defect." However, in real industrial environments, defects are often rare and appear randomly, making the process of acquiring and labeling these abnormal data extremely costly.
With AI deep learning and unsupervised learning, DaoAI AOI eliminates the need for defect samples. Instead, it trains solely on normal samples to identify features that deviate from the norm. As long as there is normal data, the initial model can be built quickly, significantly accelerating the modeling process while reducing upfront costs.
1ms High-Speed Detection for Rapid Defect Inspection
On production lines, detection speed is often a key metric for evaluating the practical value of a system. The DaoAI AOI System stands out with its highly competitive speed:
This means that no matter how many areas need to be inspected or how frequently, the DaoAI AOI System can perform defect detection with extremely low latency—keeping the entire production line running efficiently from start to finish.
Feedback Loop for Continuous Model Evolution
During the detection process, if the model makes incorrect judgments—such as falsely identifying a normal sample as defective or missing an actual defect—the system allows for immediate correction through its feedback loop. Operators can manually review the model’s output, and when errors are detected, the new labels can be quickly fed back into the model. This allows the model to continuously improve and adapt in real time.
Once new labeled data is received, DaoAI AOI can immediately retrain itself in under 1 minute and quickly update its model parameters. With each feedback correction, the model continuously refines its performace, creating a positive reinforcement loop that steadily improves detection accuracy over time.
Redefining Defect Detection—A Step Toward an Intelligent Future
In the era of smart manufacturing, automated and intelligent detection tools have become an essential part of production lines. To meet the demands of complex and dynamic manufacturing environments, companies need a fast-deploying, low-maintenance defect detection solution that can evolve over time.
With its ability to detect defects using only normal samples, high-speed detection capabilities, and an advanced feedback loop, DaoAI AOI provides businesses with an efficient, reliable, and high quality control solution.
If you're struggling with the challenges of collecting and labeling defect data or looking for a way to make your detection system smarter over time, DaoAI AOI System offers a game-changing solution.
Contact us to learn more and take the first step toward smart defect detection!