## 4 DL in Addressing Crucial Issues For Target Detection
### One-Stage Detectors in Target Detection
Moreover, it is observed that there is a need to improve the detection of the model in terms of a dark target, for example, black vehicles. With respect to the occlusion issue, this approach performs the best, but there appear to be some conflicts in the huge sector such as it shows some error that needs to be minimized. In 2021, [PERSON] et al. [67] presented a model named YOLOv4_Drone which contains the YOLOv4 algorithm based on UAV images. The detection correctness of the isolated YOLOv4 algorithm is almost low and causes errors. Therefore, to enhance the abstraction of the small targets, the receptive field block (RFB) segment [81] is included in the feature extraction phase of YOLOv4 to test the feature map and withdraw the features of various scales. The RFB segment was added to the target detection prototype and termed YOLOv4_r. There is no replication of the gradient statistics in the system optimization because this approach provides high validity while decreasing the computational complexity. In the UAV images, to solve the issue of small targets and complex backgrounds, the ultra-lightweight subspace attention mechanism (ULSAM) has been incorporated into the YOLOv4_r segment [82]. This segment derives a feature map with various attention functions for the respective feature map to represent the multi-scale function. A ULSAM segment connected to the target detection approach is called YOLOv4_u. Moreover, the soft non-maximum suppression (Soft-NMS) approach is utilized to reduce the missed target which is caused by the occlusion [80]. This is because this approach deletes the lower count frames of the two close targets if there is a huge overlap. Additionally, it remarkably decreases the statistics of detection frames. In the experimental testing, the VisDrone dataset is utilized. The various datasets of 14 areas of China based on lightning and weather situations are collected which comprise 10 kinds of targets as outlined in Figure 10. Moreover, mAP is used as an evaluation metric. From the testing, it is observed that the YOLOv4_Drone target detection approach achieved a high accuracy of 45.67% in all the weather conditions as compared to previous target detection approaches such as RetinaNet which achieved 35.95% accuracy, SMPNet achieved 35.98% accuracy, while DPNetV3 achieved 37.37%. Moreover, it is observed that isolated YOLOv4 gained 40.99% accuracy which is 5% less than the YOLOv4_Drone target detection approach. Still, the presented approach is not abundantly stable due to a moderate runtime as compared to the other target detection paradigm. In 2022, [PERSON] et al. [68] presented a YOLO-DRONE (YOLOD) model of UAV images which were upgraded on the foundation of YOLOv4 to handle the small-size objects and clustered background. To decrease the complexity of the model and obtain the best detection consequences, different activation functions were used as a backbone which comprise of Mish [83] and HardSwish [84] activation functions. To enhance the location consequences, they stimulated the convergence, and summed up the loss of the bounding box regression EIOU loss function [85]. Moreover, they utilized the pyramid pooling module in the replacement of the SPP segment and compared the model with the YOLOv4 algorithm. The aim of using the pyramid pooling module is to enhance the receptive field and performance of the detection model [86]. To boost the multi-scale feature fusion and detect the targets on various scales at the end of the model, an adaptive spatial feature fusion segment was introduced [87]. The testing was done with the help of three different datasets which included forklift, VEDAI, and PASCAL VOC datasets, where the forklift is the first known dataset deployed on UAV images.