Color Spectrum
The computer, however, does not have any limitation and it can "see" in a full wavelength spectrum, provided the right sensors.
Far Infrared
In this article, we will focus on infrared (IR), and specifically far-infrared (FIR), or long-wave infrared (LWIR).
The infrared spectrum is especially interesting because it is equivalent to the heat emitted that contrasts with the surrounding environment.
The examples of the objects visible in the infrared are:
- people
- animals
- car engines
- heaters and chimneys
- hot fluid and gas leaks
- and anything that is hotter than the surroundings
It is also worth noting that some objects, especially metal conductors can be "seen" as colder than the surrounding as they dissipate any local heat.
Not just the night vision
Many people think that infrared equates to the night vision, but that is not necessarily the truth. In the daylight, a well-camouflaged animal is equally well visible at night as in the daylight using an infrared camera.
The RGB + FIR systems.
The combination of visible light (RGB) and heat-sensing (IR) is especially useful as RGB allows us to perceive the shapes of things like terrain, vegetation, and other objects and the IR allows us to spot the people, animals and other head-emitting objects.
Practical applications
The primary goal for my research is automotive safely, detecting pedestrians and large animals may significantly reduce the number of accidents.
As a personal anecdote, I would like to bring the observation, that in Michigan where I live, there is at least one deer collision for every 50 km (or miles) traveled. This observation is obvious for anyone who travels "Up North" on weekends.
The deer, moose, bear, elk, antelope, wolf, coyote, raccoon, and many smaller animals are very difficult to detect for humans, especially at dawn or dusk in poor visibility resulting in catastrophic and gruesome collisions.
Obviously, the RGB + IR has other uses, such as search and rescue - the military and other organizations have developed this technology for decades.
Convolutional Neural Networks (CNN)
- MFNet
- RTFNet
- PST900: dual-stream (RGB and IR) method
Representative Data Collection
You can train the machine models on a relatively modest dataset, but in order for it to generalize well, the data-sets have to be vast and representative.
At this time, the open-source datasets are rare and of limited purpose as the training should be performed on a dataset taken with a specific and calibrated hardware.
Data Annotation
The collected data has to be annotated, usually by humans, in order to train and validate the results.
The biggest part of the effort
By far collecting the data sets is the most time consuming and expensive part of the effort.
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