# Experimental Solution for Estimating Pedestrian Locations from UAV Imagery
## 2 Algorithms of Pedestrian Detection from Images
### R-CNN Algorithm  
Newer methods of object detection employ neural networks to provide state-of-the-art efficiency. This has allowed modern algorithms to greatly surpass the old methods, which employ the use of detection by comparing segments of image frames. This distinction changed after algorithms, such as the region-based convolutional neural network (R-CNN) [14], were proposed. In other words, R-CNN combines newer and older techniques to optimize object detection even further. Moreover, R-CNN uses two separate forms of training to increase the robustness of the results. First, the model is pre-trained on high-quality data with supervision to accelerate the training. This is followed by unsupervised domain-specific training on low-quality data. This ensures that the model is capable of object detection (even with sub-optimal data) and prevents overfitting.  
Recognition using regions [15] is used to segment images and provide the first iteration of object detection. Objects are viewed as compounds of several parts, which together form an object. For example, in the case of detecting a car, it can be divided into several parts, such as wheels, doors, etc. These can be further subdivided, e.g., tires can be viewed as separate parts of a wheel. The process of segmentation increases detection robustness as it allows for objects to be partially obstructed. A car will be detected as a car, even if one of the wheels is not visible, as long as the rest of the regions are detected. Although R-CNN is generally much slower than most of the other state-of-the-art algorithms, the provided detection robustness and accuracy make this algorithm widely used.  
Over the last few years, R-CNN has evolved and several improved versions of the algorithm were proposed. Perhaps one of the most used versions is faster R-CNN [16], which improves the computational speed of the algorithm (e.g., used by [PERSON] et al. [17] in their proposed drone detection method). Mask R-CNN [18] provides a way to outline a detected image instead of only providing a rectangular detection window. It is also based on faster R-CNN. The efficiency of this method was proved by several types of research studies that also utilized input from UAVs. [PERSON] et al. [19] used it for the segmentation and identification of canopy. [PERSON] and [PERSON] [20] utilized it along with YOLOv3 in a bridge inspection via UAV, with an accuracy of over 90%. Despite this speed optimization, it is necessary to note that R-CNN variations still achieve lower speeds than most of the other state-of-the-art algorithms. The main benefit of using R-CNN is its robustness and accuracy. Despite the benefits, R-CNN has proven to be less efficient than YOLO [8] during experimental testing.  
R-CNN methods used on UAV output were used in research by [PERSON] et al. [21]. The authors reported close results in pedestrian detection when compared to YOLO algorithms, both having precisions of approximately 97%. Another research study [22] focused on the detection of trees, reporting a precision of 95.65%. The authors post-processed the imagery from UAVs.