Computer vision technology helps in a lot of different industries. For example, it can detect unsafe behavior on construction sites and notify human workers of dangers. It can also track animals to improve wildlife conservation efforts without disturbing them.

Gauss Surgical’s Triton line of blood monitoring solutions estimates in real time blood loss by using an iPad to capture images of sponges and suction canisters, according to this video.

Self-driving cars

Self-driving cars are no longer confined to science fiction movies. Companies such as Google’s Waymo have started offering them to customers for test rides in cities across the United States.

The company says that this technology will help you save time, reduce traffic congestion, and avoid accidents. However, there are many factors that still need to be addressed before these cars can be fully deployed.

One concern is how autonomous vehicles will handle environmental conditions. For example, how will they navigate snowy roads or tunnels? Another issue is how the cars will react to unexpected events, such as a toddler running out into the road.

To address these issues, researchers are developing machine learning to improve the accuracy of autonomous cars’ sensors. One of the biggest challenges is identifying when sensors are failing, such as in cases of electrical failure or physical damage. These systems will also need to be able to detect when they are receiving incorrect data.

Smart home devices

Whether you want to automate your home or just make it a little more convenient, smart devices can do the trick. They can help you control your thermostat, turn on the lights when you’re coming home from work, or schedule music to play while you’re cooking dinner. They can also keep an eye on your kids or your pet, and send you real-time alerts about any suspicious activity.

Generally, these devices have sensors that transfer information to hubs, which then react to the changes in environment and communicate with other connected products. For instance, if your sensor detects smoke, it can trigger the alarm system to sound, or it could activate your furnace to prevent a fire.

Other devices use geofencing to know when you’re near and can automatically unlock your door or turn off the lights. There’s even a device that measures the air quality of your home. While the smart home niche is growing, it remains a small market with relatively low adoption and regular usage rates.

Manufacturing industry

Computer vision technology has become a crucial part of manufacturing industries. It can detect surface defects like dents and scratches, and also identify and categorize products based on their size and shape. It also helps in identifying functional flaws and rejecting products that have them. This makes the inspection process faster and more efficient.

In the automotive industry, computer vision technology can help in detecting defects and quality issues. For example, it can identify welding defects and detect improper alignment of parts. This can reduce the time spent on repairing or replacing defective components. Additionally, CV can improve the efficiency of robots by providing them with precise and efficient guidance.

In addition, CV can be used to enhance security systems by monitoring people’s faces. This can be useful in schools, airports, and factories. Facial recognition can even prevent crime in some cases. However, these technologies raise privacy concerns and can be used to violate individuals’ rights, such as shaming them for violating social distancing policies or mask-wearing requirements during the pandemic.

Self-customer service

With the increasing popularity of smart home devices, customers have a lot of questions about them. This often causes problems for customer service departments because finding the issue requires a deep understanding of the device. However, companies have now started to use computer vision technology to enable customers to solve their own issues themselves. For this, they have developed smart cameras that can detect a problem and connect to mobile applications. The mobile application will then guide the customer to resolve the issue using clear and concise visual instructions. It can also monitor the customers and intervene if they are not following the correct steps.