Analysis of the use of object detection systems in edge computing

This article analyzes the potential of using artificial intelligence for object detection in edge computing environments, which are gaining importance with the growing number of Internet of Things devices. The focus is on evaluating algorithms in terms of accuracy, speed, and energy efficiency. The goal is to identify solutions that minimize latency, which is crucial for autonomous systems and surveillance. Experiments were conducted on three devices using YOLO, SSD, and Faster R-CNN models. The results highlight the most effective object detection methods in edge computing, supporting the development of industry and IoT.