## 4 DL in Addressing Crucial Issues For Target Detection
### _Two-Stage Detectors in Target Detection_
Besides this, small target detection is not fitted with a very deep network structure. Further, this approach enhances the computing cost when a huge number of categorized datasets are used. In the target detection work, to handle the multi-scale issue from the UAV images, in 2021, an ECascade-RCNN target detection framework was presented by [PERSON] et al. [91], which is the improved version of Cascade-RCNN. As a backbone, Trident-FPN is employed to extract the attributes and to boost the execution of the detectors, a modern attention mechanism scheme is presented. In addition, the K-means technique has been used to create anchors to refine the model detection and attain the best regression precision. On the Visdrone dataset, testing was performed and during the investigation, it was observed that the model achieved better accuracy in huge and small-size objects. 2. _Two-Stage Detectors for Target Occlusion:_ Due to occlusion, missed detection occurs. Therefore, it is necessary to reduce this issue. In 2020, [PERSON] et al. [92] proposed a Faster R-CNN algorithm to handle this issue. The presented approach uses different anchor fusions to choose the maximum anchor number and scale, which enhances the network perception value. Further boosting the value of network perception, they added a multi-layer feature fusion. The experimental testing was performed based on datasets of UAV images where AP, precision, and recall are employed as evaluation indicators. The results of these indicators are demonstrated in Figure 12. However, it is observed that the variety of scenarios manipulated in this approach is restricted, and changing the focus of the UAVs may cause miss-identification. 3. _Two-Stage Detectors for Complex Background:_ [PERSON] et al. [93] presented a framework composed of four different models such as (i) Faster R-CNN, (ii) YOLOv3 model, (iii) histogram of oriented gradients (HOG) + support vector machine (SVM) algorithms which are a form of machine learning [98], and (iv) for the background difference  
Figure 12: Result of evaluation metrics for target detection based on UAV dataset. technique, they chose the visual background extractor (ViBe) method. In video progression, ViBe is a dominant stochastic approach for predicting the background [99]. This proposed model handles the complicated background under various factors, for instance, wind, dozens of small and large-scale moving targets, unlimited speed, and substantial scenes. From the analysis, it is observed that the ViBe and HOG+SVM approaches do not yield satisfactory results due to restricted coherent and feature trajectory perception while YOLOv3 and Faster R-CNN perform best as compared to the other two approaches. In contrast to YOLOv3, Faster R-CNN yields the best accuracy in terms of recall and precision. However, it seems that Faster R-CNN has a crucial problem due to excessive hardware demand when implemented in the actual landscape. 4. _Two-Stage Detectors for Joint Issues_: In 2021, [PERSON] et al. [94] presented an MS-Faster R-CNN where MS stands for multi-stream which has three stages: in the stated frame, the multi-stream CNN abstract attributes at multiple scales from the target in the first step by manipulating its inherent architectural model. Second, under the Faster-CNN method, the bounding boxes close to the target are obtained using the extracted attributes map where the backbone produces CNN capability so that the area of the affinity group layer and region proposal network can turn the output of the classifier into the necessary bounding boxes. In the last stage, when the targets can be detected accurately by MS Faster-R-CNN inside of the graphical cascade, the DeepSORT [17] technique is used to attain the real-time monitoring abilities from the UAVs perception. The evaluation was performed on four datasets which include: (i) UAV20L [100], (ii) UMCD [101], (iii) UAV123 [100], and UAVDT [102].