# Comparison of Cloud-Computing Providers for Deployment of Object-Detection Deep Learning Models
## 2 Literature Review
### Object-Detection Algorithms  
Object detection is a computer-vision technique used to locate instances of objects in images or videos. Object-detection algorithms generally use machine learning or deep learning to identify objects in an image or video, classify each object by using a set of bounding boxes, and associate each object with a class probability. Many object-detection algorithms have been developed through the years by using advanced methods and models. Object-detection algorithms have a wide range of applications across various industries and domains. These algorithms are used to locate and identify objects within images or video frames. Some of the common applications of object-detection algorithms include autonomous vehicles, surveillance and security, retail and inventory management, Augmented Reality (AR) and Virtual Reality (VR), healthcare, sports analysis, and many more. Numerous object-detection models exist, including but not limited to, You Only Look Once (YOLO), Single-Shot Detectors (SSD), Faster Region-based Convolutional Neural Networks (Faster R-CNNs), and Region-based Fully Convolutional Networks (R-FCNs). Overall, each model aims to address different limitations of the previously developed models, and the choice between them depends on the specific use case.  
You Only Look Once (YOLO) is one of the most popular object-detection algorithms used widely in various object-detection use cases. Ref. [16] performs an in-depth analysis of the various YOLO versions throughout the years and compares most of them by using Microsoft Common Objects in Context (COCO) as the standard dataset. The authors discuss that, over the years, most YOLO versions after YOLOv4 and YOLOv5 had a trade-off between speed and accuracy. For example, the latest version named YOLOv8 resulted in a higher Average Precision (AP) of 53.9% against the 50.7% of YOLOv5 on the same inputs. Similarly, ref. [17] develops YOLO-LITE in order to provide a combination of relatively high accuracy and speed to non-GPU computers. It achieves an AP of 12.26% with a speed of 21 Frames Per Second (FPS) compared to Tiny-YOLOv2 with its AP of 23.7% and speed of only 2.4 FPS, which is extremely slow for real-world applications.  
For the purpose of this paper, we are going to use the YOLOv8 object-detection model. This model is the latest version of the YOLO series and has faster inference times and a higher expected accuracy as in [16]. This model has applications across various domains including healthcare, smart cities, and Unmanned Aerial Vehicle (UAV) detection. First, ref. [18] presents a novel real-time Arrhythmia-detection application by using YOLOv8, showcasing its ability to identify irregular heart rhythms, thereby facilitating timely interventions. Next, ref. [19] proposes an improved fire-detection approach for smart cities by using YOLOv8, where its real-time object detection proves instrumental in identifying and localizing fire incidents promptly. Finally, ref. [20] introduces a UAV detection method based on YOLOv8, demonstrating its effectiveness in enhancing security measures by identifying and tracking small unmanned aerial vehicles. Overall, YOLOv8 emerges as a pivotal tool, addressing complex challenges and driving innovation in real-time object detection across healthcare, urban planning, and security domains.  
The choice of model, however, has no significant bearing on the expected results. The aim of this paper is to measure the relative performance of their deployment on a cloud platform and not necessarily their prediction accuracy. Hence, metrics such as the mAP (mean Average Precision) and Intersection over Union (IoU) are not relevant for our purposes. Our selected YOLOv8 model will be deployed onto the different cloud platforms to test the various metrics mentioned in the previous sections.