AI Cameras: Can They Replace IoT Sensors?
Okay, maybe we’re being a little misleading; by AI cameras, we actually mean computer vision. The recent progress in imagery analysis powered by artificial intelligence makes us wonder if the camera could in fact replace several types of sensors. For numerous use cases, it makes the hardware far easier to manage and provides more insightful data. Visual intelligence has much more to offer than the human eye. It can observe hundreds of places at the same time, zoom down to a submillimeter scale, see in infrared, and much more. Many things currently being monitored by sensors (temperature, movement, proximity) could be verified and, actually, improved by an AI-powered camera.
This article will try to answer, considering your own use case, whether it is best to replace sensors with cameras.
Advancements in computer vision and AI-enabled cameras mean that they are better able to perform functions today.
With the democratization of HD and 4K cameras, shooting at 60-120 fps (frames per second) or more, computer vision has proper tools to analyze real-time footage better than ever.
Uses such as reading the plate number of a fast-driving car require a rolling high speed to catch a clear image. Full frame sensors and 4K make it possible to monitor a large vision field with only one camera.
High-end cameras, especially on smartphones, are an easy and qualitative source of data acquisition, directly in the field. When it comes to industrial maintenance, any technician has a tool in his pocket to upload an image or video and consult an AI to find the solution to the issue. In agriculture, a farmer can take a picture of a crop and immediately have information about a potential disease.
Drones have also become an important part of computer vision, especially for agricultural or large industrial installations (power lines, recycling plants, pipelines, etc.). The ability for drones to fly over large areas means that cameras can collect images that would have been far too cost-prohibitive even a few years ago.
Video feeds require more storage space than most sources of data. The cost of uploading several 4K footage continuously on the cloud could be a roadblock for using video. Edge infrastructure addresses this challenge by analyzing the footage locally and uploading only a fraction of its data for further analysis.
With video, data privacy and security are extremely sensitive, especially compared to devices such as agricultural soil sensors. Storing the files locally on edge devices can reduce the risk of their hacking, but above all clarify the responsibilities in the case of data robbery (site manager, client).
We see the world through our human eyes, which allow us to see with the precision of vision around 30 degrees, at a reasonable distance and appropriate lighting conditions. Cameras are far less limited than that. Especially when several cameras are combined, computer vision has almost no limitations. It can see from several angles and analyze those different feeds in real-time.
