## 4 DL in Addressing Crucial Issues For Target Detection
### One-Stage Detectors in Target Detection
Figure 7 demonstrates the recall outcome of target classes in three test videos recorded by high-depiction cameras; similarly, Figure 8 represents the precision outcome of the target classes in the same test record videos. Figure 7: Recall calculation of target classes on three captured test datasets. In 2022, [PERSON] et al. [63] presented a YOLOv4-based approach to detect small targets in terms of cars and pedestrians on the VisDrone dataset. To enhance the performance of the presented model, the KCF algorithm and average peak-to-correlation energy scheme were utilized to stabilize the model and track small targets of long interspace. 2. _One-Stage Detectors for Target Occlusion:_ In 2020, [PERSON] et al. [64] presented a YOLOv3, soft non-maximum suppression (Soft-NMS), and K-means++ framework to handle the moderately occluded targets in the region of the UAV portrayal. The K-means++ method is used in the YOLOv3 algorithm to optimize the series of the first recognition box and improve the AP estimate of the network [79]. Later, the Soft-NMS method was executed to solve the issue of multi-box crushing by NMS to improve the AP estimate of the network [80]. During the training operation, overfitting occurs due to some training feature samples. To lessen this issue, data augmentation was implemented which comprises of color oscillation, arbitrary rotation, and image flip. For validation, three generic datasets were selected with various image characteristics to enhance the network. Based on the experimental results, it is observed that the upgraded YOLOv3 method achieved high accuracy and a fast detection rate. The results of the three datasets in terms of average precision (AP), precision, and recall metrics are presented in Figure 9.  
Figure 8: Precision calculation of target classes on three captured test datasets. Figure 9: Result of YOLOv3 approach on different datasets. 3. _One-Stage Detectors for Complex background:_ In [65], [PERSON] et al. presented a gradient classification prophecy branch in the head network of YOLO to produce angular data and utilize the circular rectify class to overcome gradient classification loss and detect the target under complex circumstances. Moreover, to improve the UAV images, they implemented the data augmentation approach which consists of rotation, arbitrary flip, translation, and HSV augmentation. Then, they presented the cross-stage partial bottleneck transformer (CSP BoT) segment which is a hybrid approach that uses the multi-head self-attention process convolutions to encapsulate the latent broad spatial correlation of the target in the UAV images and improve the critical information. Finally, they adopted the general characteristics at various resolutions and predicted the spatial disparity in ambiguity by the weighted cross-scale interconnection. The adaptive spatial feature fusion-Head block was presented. The enormous experimental outcomes on UACS-AOD and UAV-ROD datasets show the presented model's dominance, low design complexity, and cost-effectiveness. 4. _One-Stage Detectors for Joint Issues:_ [PERSON] et al. [66] proposed a YOLOv4 approach based on K-means clustering to recognize the multi-resolution detection scheme. The drone collected data from a low-elevation aerial viewpoint which comprises of various data such as heights, size, and positions. Moreover, they applied a data enhancement approach to improve the robustness of the target model. This approach has two main parts: one is color transformation such as brightness, tinge, and contrast and the other one is geometric transformation such as rotating, arbitrarily clipping, flipping, and splicing. A Darknet scheme is used in the training process. Mean average precision (mAP), recall, and precision are evaluation metrics used for the assessment. During the experiment, it was observed that the target model's accuracy reaches up to 95% while on the same dataset, the accuracy reaches up to 96% in cloudy weather.