# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 3 Sensing System
### Sensing Technologies
#### 3.1.3 Optical Sensors  
Optical sensors detect electromagnetic waves in the range of frequencies from infrared (300 GHz) to ultraviolet (790 THz). It is a passive technology, therefore with low energy consumption, which can provide two-dimensional images of the surrounding environment. Optical sensors can be divided into two main categories, depending on the frequency band in which they work: visible (VIS) or non-visible. For example, the first category includes optical cameras, which can detect electromagnetic radiation in the 430-790 THz frequency range, while the second category includes thermal cameras, which convert infrared radiation (300-430 THz) into images. Thanks to image processing techniques based on computer vision (CV), it is possible to detect, classify and track drones that fall within the field of view (FoV) of the optical sensor. The aforementioned operations are similar to object detection and tracking, which is a much-studied problem in the CV field. An object can be detected thanks to features such as geometric shape or outline and features related to movement between consecutive frames. In the event that the sensing system is mounted on-board a drone, the dynamism of the scene must be considered, which introduces problems of variation in lighting conditions and background characteristics. The sensors that operate in the visible frequency band show their limits in the case of adverse weather conditions (rain and fog) and the case of low ambient light such as at night. Infrared (IR) sensors allow us to overcome these limits ([46,47]) and offer greater robustness against rapid changes in illumination [48]. Two types of existing approaches in the literature are essential [49]: direct or feature-based techniques and machine learning methods. The first category includes algorithms that try to identify a specific region of interest in the image by looking at the best similarity with a reference representation of the target. In direct techniques, information on the local gradient is used in each pixel of the image, while in feature-based techniques, features are used that are followed in a sequence of frames through specific descriptors. The detection and tracking capabilities are good, as highlighted in [50], where the processing is based on techniques of background subtraction and optical flow calculation. Machine learning techniques are very popular among the scientific community. The training of a neural network for target detection and classification is one of the most studied fields of CV. In fact, given the great accessibility of standard optical cameras, this research area has reached a fairly mature stage. Thanks to the large availability of public image datasets, UAV detection challenges are often organized at international conference workshops, such as Advanced Video and Signal-based Surveillance (AVSS) conference and International Conference on Computer Vision Systems (ICCVS). Image classification with the deep learning paradigm is one of the most active fields of research. Most of the works that employ deep neural networks (DNNs) for drone classification problems utilize a generic object detection architecture, with a powerful DNN as a classification model targeted for drones. The most used architectures are:  
* Single-shot multi-box detectors (SSD)
* Faster R-CNN  
A particular SSD architecture is the You Only Look Once (YOLO) model, which has gained great popularity thanks to a particular computational efficiency that allows its use also on embedded systems in real time. The adoption of different CNN architectures (e.g., Zeiler-Fergus (ZF), visual geometry group (VGG16)) for drone detection has been investigated in [51]. To overcome the limited amount of data available for training the deep networks, authors exploited transfer learning from ImageNet and performed a pre-training to fine-tune the models. The experimental results revealed that VGG16 with faster region-based convolutional neural network (R-CNN) achieved the best performance among all the considered architectures.