# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 2 UAV Detection and Identification
### Vision-Based Detection
#### 2.3.2 Deep Learning Based Object Detection  
RCNN  
It is easy to understand how RCNN (Regions with CNN features) [62] works. Initially, selective search is used to extract an object candidate frame set. An ImageNet-trained CNN model is then applied to each candidate frame and rescaled to a fixed-size image, such as AlexNet, to extract features. The final step is to determine whether objects are present or absent within each region and to identify object classes using a linear SVM classifier. For instance, RCNN has demonstrated a substantial increase in mean Average Precision (mAP) 8.5%. SPPNet  
SPPNet (Spatial Pyramid Pooling Network) was proposed by [PERSON] et al. [63] in 2014, which addressed the limitations of fixed-size inputs in CNN. A major contribution of SPPNet is its Spatial Pyramid Pooling (SPP) layer, which allows CNN to generate fixed length representations without rescaling depending on the image size. This approach avoids the need for repeated computation of convolutional features by computing the feature mapping only once from the entire image. When used for object detection, SPPNet enables the generation of a fixed-length representation for any region, making it unnecessary to rescale the image or region of interest. Using this approach, SPPNet can detect objects at 20 times faster speeds than RCNN without compromising detection accuracy (VOC07 mAP = 59.2%). [PERSON] et al. introduced a faster RCNN [65] detector shortly after the Fast RCNN detector made its debut in 2015. The Faster RCNN detector is considered to be the first to detect deep learning end-to-end and in near real-time. It achieved a COCO mAP@.5 of 42.7%, COCO mAP@[.5,.95] of 21.9%, VOC07 mAP of 73.2%, and VOC12 mAP of 70.4%. By integrating proposal detection, feature extraction, and bounding box regression into a single framework, Faster RCNN detector provides a complete solution for object detection. Today, faster RCNN is widely used for object detection due to its enhanced speed and accuracy. YOLO (You Only Look Once) was proposed by [PERSON] et al. in 2015 to detect objects in one stage [66]. YOLO is known for its remarkable speed, with the fast version of YOLO running at 155 fps (VOC07 mAP = 52.7%), while the enhanced version runs at 45 fps (VOC07 mAP = 63.4%, VOC12 mAP = 57.9%). Unlike previous object detection methods that used a \"proposal detection + verification\" paradigm, one neural network is applied to the whole input image in YOLO. An image is divided into a grid of cells, with each cell containing its bounding box and probability. However, when it comes to localizing small objects, its accuracy falls short compared to that of the two-stage detector. YOLO is very popular in the field of UAV detection because of its extremely fast speed and relatively high accuracy, and there have been many studies on UAV detection based on the YOLO algorithm in recent years [67; 68]. [PERSON] et al. presented SSD [69] in 2015 as the second level 1 detector in deep learning. This invention introduces multi-reference and multi-resolution detection techniques that greatly enhance detection accuracy, especially for small objects. SSD outperforms previous detectors in terms of speed and accuracy, with VOC07 mAP of 76.8%, VOC12 mAP of 74.9%, COCO mAP@.5 of 46.5%, mAP@[.5,.95] of 26.8%, and a fast version running at 59 fps. SSD detects objects at 5 different scales at different layers of the network, as opposed to previous detectors that detected at only the top layer. RetinaNet  
RetinaNet [70], proposed by [PERSON] et al. in 2018, is a one-level target detector that overcomes the challenge of training a single-level detector with an extreme foreground-background class imbalance. While two-level target detectors can achieve higher accuracy by proposing dense candidate locations, single-level detectors have been limited by difficulties in training an unbalanced set of positive and negative examples.