In our previous article, "What is computer vision," we mentioned that computer vision is a key capability of artificial intelligence.
However, computer vision currently has its drawbacks: even after extensive data training, classifiers still make mistakes. So how can we improve the accuracy of computer vision? And let it play a bigger role?
Improve computer vision accuracy
Researchers have used a trained AlexNet neural network to create another AlexNet network that can identify guitars, and through a confidence rating, the latter draws a guitar image with 99% confidence.
This shows that neural networks have great potential. Through continuous improvement, we can achieve satisfactory results with less data.
With a well-trained model, and high-relevance data to improve accuracy, the future of computer vision applications is limitless.
Make life safer
Computer vision can make our lives safer. For example, by using computer vision to scan irises and fingerprints, we can more easily manage people entering office buildings, and we can retrieve people’s medical records or criminal records.
This does bring about potential data privacy issues - this is why Qualcomm is focusing on terminal-side artificial intelligence: Keeping user data on the device greatly reduces privacy risks.
In terms of network security, computer vision has also played an important role. For example, on some video sites, when a user attempts to upload terrorist content, the artificial intelligence system will analyze whether it matches a known terrorist video, once the match is confirmed. , users will not be able to upload.
Optimize infrastructure
Computer vision can also improve the quality of roads. By allowing the machine to detect surface micro-defects faster and more accurately, we can improve road acceptance efficiency.
In addition, computer vision can also optimize urban traffic management. Previously, we could only determine which road sections were dangerous after an accident. But computer vision can identify those potential traffic accidents and classify them. With these data, we can take more proactive measures to avoid traffic accidents. Decision-makers can also use these data in planning to avoid building high-risk intersections.
With the increasing capacity, computer vision can eliminate many of the problems in our lives, allowing us to focus on the most important things.
Qualcomm has been working hard to create “on-call†terminal-side artificial intelligence while also working on more efficient artificial intelligence hardware.
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