# An Intelligent Real-Time Object Detection System on Drones
## 2 Related Work
### Object Detection  
Object detection aims to locate and classify objects and we introduce some classic object detection algorithms in this section. With the development of deep-learning technology, the performance of neural networks in object detection far exceeds traditional algorithms, and the state-of-the-art models on public datasets such as MS-COCO [12] are all based on neural networks. The object detectors of the current mainstream object detection algorithms can be divided into two-stage object detectors and single-stage object detectors. The R-CNN family, such as Fast R-CNN [13], employs two-stage object detectors. Yolo [14] and Swin transformer [15] use a single-stage object detector. These models can achieve quite a high accuracy and real-time inference speed in object detection, but the number of parameters is large and the training time is long. Due to limited resources in some scenarios, we cannot deploy these models on edge devices, so there are many lightweight models such as MobileNet [16].  
R-CNN [17] is the first application of convolutional neural network (CNN) in the field of object detection, where CNN is mainly used for feature extraction. R-CNN uses the selective search method to select 2000 object candidate regions, then uses CNN to extract a 4096-dimension feature vector from each region and constructs SVM classifiers to classify the feature vectors, and, finally, uses non-maximum suppression (NMS) to adjust the position of the candidate regions.  
Compared with R-CNN, Fast R-CNN [13] uses a convolutional neural network as the output to classify and predict the position size of the box, replacing the original SVM classifier. In order to improve the calculation speed, the network finally uses SVD instead of the fully connected layer, so the training speed of the latter is faster than the former.  
Yolo [14] treats detection problems as a regression task to directly predict pixels as targets instead of a classification task. Firstly, Yolo divides the input image into SXS grid cells. Each grid cell will detect whether the center of the object falls into it, and if it falls, the grid cell is responsible for detecting the object. Then we can obtain multiple bounding boxes (BBoxes) and the confidence score of the boxes from each cell. If there is no target, the confidence score is zero.  
Backbone architectures are an important component in the object detector to extract the input image features. The ResNet [18] proposed by [PERSON] et al. is one of the widely used backbones in object detection. ResNet avoids the vanishing gradient phenomenon through the cross-layer connection of the neural network, and this connection method does not bring additional computational complexity. There are two basic blocks in ResNet-50, Conv Block and Identity Block. The input dimension and output dimension of Identity Block are the same, which are used to deepen the network depth. There are 16 bottleneck modules and a 7X7 convolutional kernel, each bottleneck module has three layers of convolution. The ResNet-50 network contains 49 convolutional layers, and a fully connected layer.