Applications and Potential Risks of Computer Vision Systems

People often wonder what it would be like if computers could think. Computer vision is about enabling them to see. Not literally, of course. But the field of computer vision allows machines to understand the content of visual inputs and take action based on that information.

It’s pretty remarkable, really. Humans have complex physiological systems and years of context that enable them to distinguish between different types of objects. Most of us take for granted the ability to judge distance, perceive motion and understand other visual clues. Computer vision systems must replicate this ability with processors, cameras, algorithms and lots of training data.

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Once trained, however, a computer vision system can perform remarkably well. It can observe a production line tirelessly and spot a problem in a split second. It can take in all the visual information from a security camera and quickly identify a security threat. These capabilities eliminate the need for humans to perform these tasks while improving speed and accuracy.

Thanks to these benefits, computer vision is seeing rapid adoption. MarketsandMarkets expects the global market for computer vision to see a compound annual growth rate of 21.5 percent through 2028.

How Computer Vision Works

Like most advanced AI technologies, computer vision utilizes deep learning and neural networks. However, the neural networks used for computer vision employ a technique called “convolution” in one or more layers. Convolutional neural networks are designed specifically to examine the pixels in an image and predict what they mean. Over time, the neural network learns to “see” images. Similarly, a recurrent neural network enables computers to understand the frames in a video.

The computer vision model can then perform several tasks:

Image classification is the ability to predict what type of object is depicted in the image. Is it a person? A car? A tree?
Image annotation involves the automatic tagging of images with metadata.
Content-based image retrieval capabilities enable the system to search for and retrieve images based on their content rather than metadata.
Object detection is the ability to identify a certain type of object in an image.
Object tracking follows an object in motion after it is detected.

These capabilities have a host of applications in industry. In healthcare, for example, computer vision is used to analyze X-rays and other medical images, examine tissue samples and more. Computer vision is a key element of self-driving vehicles, road condition monitoring and parking occupancy detection. Manufacturers use computer vision to aid in product assembly, detect product defects and identify safety hazards. In agriculture, computer vision can classify produce based on various qualities.

Woman utilizing her computer vision system

Potential Risks

The latter applications of computer vision are fairly straightforward. If the system is looking for deformities in metal or whether a product was misassembled, there’s a limited data set involved. Plus, the outcome is pass/fail. Did the system detect a crack? Does the product look accurate? Computer vision is very beneficial in that context. It can perform these tasks much faster than humans and readily scale.

Other applications of computer vision come with greater risk. When using computer vision to analyze medical imaging, you must keep the data secure and ensure that you meet HIPAA compliance requirements. Most importantly, you have to consider your training dataset and the risk of false positives or negatives. For example, if the system is designed to detect cancers and learns from images of stage three and four cancers, it’s not going to detect earlier stages.

The DeSeMa team understands the benefits and challenges of computer vision and its application in specific use cases. Let us help you take advantage of this technology while minimizing the potential risks.