# Advances in Neural Network-Based Image, Thermal, Infrared, and X-Ray Technologies
## 2 Literature Review Methodology and State-of-the-Art
### State-of-the-Art: A Technology Review from 2005 to 2024
#### 2.3.3 Infrared Detectors  
The spectrum of application of infrared detectors is very wide. It could start from basic usage, such as helping in works of others sensors. In [78], detectors were used to calibrate an RGB camera to take a correct photo. However, infrared detectors often work in combination with different data sources. The main domains of this integration concentrate around mobile robot navigation [79; 80; 81; 82; 83; 84; 85; 86], object recognition [87; 88; 89; 90], position detection [85; 91], human measurement [92; 91] and machine works improvement [93]. Integration of a multi-source navigation system in mobile robots gives robust control mechanism of them. The first application of infrared detectors is to build a map of an environment in which a robot exists [85]. In connection with ANNs, this is a more economic solution than a laser scanner [94]. The next example is to use them to navigate a robot to avoid obstacles that are detected [80; 95]. In addition, its quality could be improved using sonar data to measure a distance and RGB camera to detect a road sign [79]. To faster prepare a robot to do this, neuroevolution techniques are used [96; 97]. They set weights in neural network, meaning that training them is faster. The retro-reflective characteristics of lane demarcations on roadways allow them to be easily detected in infrared light [98], which simplifies their tracking. This problem is frequently investigated with some configurations: driving along a line [81; 99], measuring a distance [81], or planning a path to drive [82]. Because using mobile robots with AI is becoming a more and more common application, there is a need to educate a new staff of robot programmers. An educational training platform which provides an opportunity to learn how to train neural and Bayesian networks using infrared detectors, among others, was described in [86]. Another important field in the use of infrared detectors with artificial intelligence is positioning, which can involve a few aspects. The first is to detect all nearby objects and build a map [85; 94]. This could also include ground properties [100] or tracking a navigation path [101]. This feature does not have to limit itself only to an open space, and could also be used in another object such as the human body [78]. Finally, setting some object in the proper distance and position between each other could be achieved in the same way, which helps to obtain better quality data for other sensors [91]. RGB images are used very often as input data to recognise an object. In addition, within this scope infrared detectors could play a crucial role. As mentioned previously, they can recognise an object in the way of mobile robots [87; 99]. To achieve a better result, these data can be mixed with information from other sensors, e.g., radars [88]. In addition, detecting lines [98] or cars [90; 102] is more accurate. Particularly if light conditions are weak, such as at night [98; 103], this method makes it possible to detect and classify ships [89]. It is not always light intensity that is the only problem. Sometimes an object looks very similar in visible light. Then, using infrared could help to separate them, such as during the detection of foreign fibres in cotton [104]. This ability is also used for recognising cars visible from a plane, where resolution is poor [90; 102], or for recognising humans at night [105; 106]. At night, recognition of a ship is more difficult, and this processing in infrared is applied. In effect, classification of ships is possible, although training and real images have a different palette of colours [103]. Another way to solve this problem is to connect these data with an RGB camera [89]. Detecting a human in infrared light is only one example of a whole group. Another is a classification of their activity [106]. In this case, avoiding visible light increases privacy because it does not allow for the identification of a specific person. The next is a diagnosis of an illness, for example, COVID-19 [92].