## 4 Avinomics Assessment
### Artificial Intelligence (AI)-based techniques
#### 4.1.2 Target Tracking  
Within a UAV's field of vision, target tracking refers to its process involving the identification and localization of certain objects or points of interest [158]. Such a procedure begins with data capturing (i.e., images of the surroundings) [159] through the embedded cameras. Obtained data can then be pre-processed in order to ameliorate its quality, therefore rendering the target tracking algorithm more accurate [160]. The target tracking algorithm afterward identifies objects of interest in the processed data, thus providing class probabilities and bounding box coordinates [161]. After refining the results through filtering techniques [162], the UAV's path control can be adjusted accordingly [163]. In this context, regression-based approaches aim to compute the object's correlative and class probability, including YOLO [164], YOLOv2 [165], YOLOv3 [166], YOLOv4 [167], and YOLOv5 [168]. On one hand, YOLO aims to detect small objects in real time by identification in image frames. The input image (i.e., taken by the UAV's camera) is divided into a grid, where, for each of the grid cells, the algorithm predicts the class probabilities, bounding boxes, and object scores. Bounding boxes represent the potential locations of objects inside the cell, and class probabilities are then assigned to each bounding box (i.e., the likelihood of an object belonging to a particular class such as a car, person, etc.). At the last stage of the YOLO algorithm, the certainty regarding the object inside a box is calculated after multiplying the class probability with the confidence score. Despite its real-time image processing capability, YOLO may face challenges in adapting to dynamic environments, localizing small objects, and handling overlapping objects [169]. With the drawbacks of YOLO, YOLOv2 represents an updated version with enhanced bounding box predictions through the implementation of anchor boxes. Additionally, YOLOv2 can handle more object categories because of its incorporated hierarchical classification with enabled joint training on multiple datasets. It can be therefore stated that YOLOv2 detects more diverse object classes than YOLO with an improved overall accuracy. Still, YOLOv2 suffers from overlapping object suppression difficulties [170]. Successively, as an improvement of YOLOv2, YOLOv3, consists of a hybrid architecture composed of Darknet-53 [171] and ResNet [172]. It detects multi-scale objects via a feature pyramid and integrates the regression of the bounding box, class prediction, and score calculation in a single forward pass. Despite its increased effectiveness over its predecessors, YOLOv3 still lacks temporal information consideration, making it less suitable in dynamic environments [173]. The consecutive YOLOv4 is mainly characterized by three grids as follows: backbone (utilizes the CSPDarnet53 classifier), neck (a parameter assembling approach within lessen the information trajectory), and head (same as in YOLOv3). The evaluation target in YOLOv4 is improved compared with its predecessors via Generalized Intersection over Union (GIoU), which is used as a loss function [174]. As compared with YOLOv3, the backbone architecture in YOLOv4 enhances feature extraction with improved accuracy because of its progressive training with larger image sizes [175]. Forwardly, YOLOv5 represents a more streamlined model since it removes the CSPDarknetet53 backbone, in addition to focusing on model size reduction [176]. As a visual interpretation, Figure 3 presents the graphics of the same objects detected by YOLOv3, YOLOv4, and YOLOv5 [177], where the boundary boxes become more precise with the evolution of each YOLO (i.e., by means of YOLOv5, the boundaries of each tennis court become tighter and more realistic with respect to the predecessors, higher degree of confidence of the objects' classes, etc.). Moreover, YOLO-based target tracking algorithms kept evolving toward YOLOv6 [178], YOLOv7 [179], and YOLOv8 [180], aiming to have better object detection accuracy than each predecessor.