## 5 Discussion and Future Research Trends
### Remarks  
Based on the previous consideration, we want to give further details to figure out what type of DL approaches are good for resolving what types of problems:  
* In Faster R-CNN, the information about small targets will moderately disappear as the network progresses. Therefore, for very deep network structures, small target detections are not good enough. * The detection speed of the two-stage detectors is slow as compared to one-stage detectors which is the major defect in target detection. On the other hand, it is observed that YOLOv3 achieved high accuracy and a fast detection rate as compared to other YOLO versions for small-size objects but still possesses high computing complexity in real-world performance. * One-stage detector performs best in the occlusion issue while in the occlusion of large regions, the model still shows some misconception. * In the YOLO algorithm [18], small targets in the form of groups are hard to handle because the generalization capacities of the model are poor, and loss function issues easily cause prominent positioning miscalculations. * Faster R-CNN performs best compared to YOLOv3 in the complex background but it is noticed that the excessive hardware need is a serious issue. So, according to the  
Figure 16: Classification of One-Stage Detectors in DL. Figure 17: Classification of Two-Stage Detectors in DL. particular complex situations, how to execute the Faster R-CNN on moderate-size hardware with wide prospects is also another issue. * Overall, it is also observed that it is crucial to obtain real-time identification in two-stage detectors because the accuracy is high while the calculation amount is still huge. * For joint issues, the accuracy of the YOLOv4 algorithm is improved in contrast to other approaches but its speed is moderately decreased. * _Future Research Trends_
* UAV's vision-based approaches for the target detection are highly remarkable in contrast to traditional approaches. Through UAV's target detection, the UAV vision-based approaches, recognition and detection encryption are important parts of it. So, for the future direction, it is necessary to perceive more appropriate recognition and detection encryption. Moreover, it is necessary to enhance the UAV vision-based model accuracy for the desirable outcome and to overcome the existing conflicts. * To reduce the number of false alarms during the tiny target detection for railing broadcasting of surveillance, it is demanded to deploy the various visual sensors and different perspective stereo visions with the help of multi-sensor collaboration. * In terms of power consumption, it is compulsory to propose efficient approaches and present the advanced function offloading methods which can discover the light-leverage batteries that can notably increase the flight duration of UAVs, endure the prolonged distance, and decrease the total power consumption to carry out the predefined functions in the future. * In the future, it is mandatory to optimize the proposed algorithms by the use of laser facts and depth maps etc., to record more productive image characteristics and detect small and dense obstacles. * The YOLOv4_Drone approach yields a 5% to 15% higher detection accuracy as compared to other models such as CenterNet which has 29.85% detection accuracy but in terms of real-time implementation and speed, it is required to improve the YOLOv4_Drone approach in the future. * The bounding box associated with the pedestrian category is extracted from the concluding measurement in the TAU approach and has some limitations in the current design. In the future, it is obligatory to replace the current design of YOLOv3 with the latest version of YOLOv4 and an online DeepSORT target tracker with a multi-object tracker to solve these issues with efficient results. * In the area of aerial photography, developing an extensive and versatile dataset is a major challenge. So, it is obligatory to gather high-standard datasets to ease this challenge.