# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 4 Results
### Real-Time Object Detection Algorithms  
Based on our review of the selected literature, three categories of algorithms have been employed for real-time detection in unmanned aerial vehicle (UAV) remote sensing: traditional methods, machine learning, and deep learning. Previous surveys [8; 12; 21] have indicated that deep learning stands as a pivotal approach for achieving real-time detection in UAV remote sensing owing to its widespread application in image detection. The findings of this study further corroborate this assertion. With the exception of one reference that does not explicitly outline a specific algorithm, among the remaining literature, 60 works utilized deep learning algorithms, 5 employed machine learning methods, and 11 adopted conventional approaches. Figure 8 illustrates the distribution of specific algorithms utilized within each category. The so-called \"traditional methods\" predominantly refer to approaches grounded in digital image processing techniques and analyze characteristics such as the morphology and colour of the target object. To elaborate, articles utilizing feature-matching methods [28; 72; 81; 93; 109] are encompassed within this category, including a geometric approach for matching morphological features [93]. Additionally, segmentation methods based on colour [57; 73; 86] have also  
Figure 8: Classification of different real-time detection algorithms. The tree map shows the specific algorithms in each category and the number of uses. been mentioned. Furthermore, studies targeting non-RGB sensors incorporate techniques such as signal processing, as seen in applications such as the water-filling method [84]. Some research focuses on computing vegetation indices [68; 69], which can be calculated by using the required bands to obtain the detection results. Regarding machine learning, most research used the support vector machine (SVM) algorithm [37; 60; 79; 110]. While deploying SVM alone usually does not achieve the requirement of real-time detection, in these studies, commonly modified or added methods were adopted to speed up the algorithm, such as the bag of features approach, whose origin stems from the bag of words model and which was used to simplify the model [79]. Moreover, research using multi-class classification has also been documented [96]. In recent years, deep learning has found extensive application in image detection tasks. However, it also imposes significant demands on computational and storage resources, rendering its deployment in real-time tasks challenging [21]. As a result, there is often a need to streamline deep learning models. In our investigation, we identified 4 articles [42; 54; 63; 77] utilising custom-designed networks, while the remaining 56 references optimized pre-existing models for application in real-time detection tasks within UAV remote sensing. Deep learning-based object detection has been categorized into two-stage and one-stage algorithms [111]. Although two-stage detection was successful in the early stages, its speed has been an important challenge. A one-stage object detection algorithm takes the entire image and passes it through a fixed grid-based CNN rather than in patches. Among the literature optimizing pre-existing models, the majority of the studies tended to favour the selection of one-stage models. Notably, the YOLO (You Only Look Once) model, in its various versions, represented a substantial portion with a total of 35 articles. The versions of YOLO included, for example, YOLOV3-Tiny [94], YOLOv4 [43], YOLOV5 [97], etc. Additionally, another single-stage detection algorithm, SSD (Single Shot Detection) [40], was featured in 3 articles. Two studies were reported to employ two-stage detection algorithms, specifically RCNNs (regions with convolutional neural networks) [83]. Furthermore, several other deep learning algorithms with light-weight architectures have been used for real-time object detection, including MobileNetV2 [36; 41; 52; 95], U-Net [66; 67; 74; 76], ResNet18 [38; 65], AlexNet [18; 44], FCN [58], and SegNet [71].