## 3 DL for Target Detection
### One-Stage Detectors  
Figure 6: The main prototype of the DL family utilized for target detection. The one-stage detector is also known as a regression-based approach which directly computes the object's correlatives and the class probability and then produces the outcomes after an isolated detection and enormously increases the detection speed. Some frequently used one-stage detectors for target detection through UAVs are stated below. * YOLO was introduced by [PERSON] [21] in 2015. The major aim of this approach is to detect small objects and compute their fast speed. Through an artificial neural network ANN, this algorithm takes out the image attributes and then utilizes the regression algorithm to execute the image detection effect. With the help of a neural network, it can instantly extract the classification and locality of the bounding box. As a backbone, Darknet-19 and GooleNet is used in the training network, while confidence loss is utilized as a loss function. The grid segment is answerable for target detection. This algorithm has vigorous generalization capabilities because it can understand the highly versatile features to delegate to other regions. * YOLOv3 was presented by [PERSON] and [PERSON] [21] in 2018 which is the updated version of YOLO. As a backbone, this version uses the Darknet53 classifier and utilizes a multi-scale indicator. Feature extraction is carried out with the help of Darknet-53. There are 53 convolutional layers to train ImageNet. The feature bounding is downward-sampled because convolutional layers are a two-step process and, at three various dimensions, it executes detection. Meanwhile, to verify the normalization of the input in intense layers with the help of convolutional layers, batch normalization is illustrated. In contrast to Darknet-19, Darknet53 shows superior accuracy. Besides this, to overcome the over-fitting, Leaky RELU can be utilized. Through extra convolutional layers, this version seized the low measure feature which boosts the small targets and other issues as well as enhanced its speed. Moreover, by contrasting the prediction outcomes with the actual merit of the sample class, the loss value is gained, and the framework variables are updated with the help of a back propagation design to get the boost network prototype for target detection. The loss function is the aggregate of three distinct losses which are: (i) confidence loss, (ii) classification loss, and (iii) location regression loss as shown in Equation (1). \[L=\lambda_{1}Loss_{conf}+\lambda_{2}Loss_{cla}+\lambda_{3}Loss_{loc}\] (1)
* YOLOv4 is the latest version of the YOLO group, which was presented by [PERSON] et al. [22] in April 2020. This version is the improved version of YOLOv3 and is more marvelous than YOLOv3. This model is categorized into three parts: (i) backbone grid, (ii) neck grid, and (iii) head grid. The CSPDarnet53 classifier is utilized as a backbone grid which is the combination of Darknet53 and CSPNet [51]. With extra modules, convolutional and bath normalization layers are attached after the backbone grid. Further, the Mish activation function and spatial pyramid pooling are manipulated to enhance the correctness of the feature output and generalization capacity of the network [52]. Moreover, the main advantage of these layers is to enhance the difficult multi-target depiction experiment. In the neck grid, to lessen the information trajectory for the various detectors, a path aggregation network and FPN (feature pyramid network) are operated as a parameter assembling approach [53]. The head grid still employs the head grid of YOLOv3. Additionally, GIoU (Generalized Intersection over Union) is used as a loss function to improve the evaluation consequences of the target and optimized the model based on various factors such as illumination situation, height, dimension of the object, occlusion gradation, etc. Moreover, GIoU measures the intersection proportion between ground truth and bounding boxes of prior mount and prediction mount.