Object detection in computer vision involves identifying and locating objects within an image, a video frame, or other visual content. This process includes determining the position of the object and assigning a label to it. Object detection provides detailed localization and classification of objects within an image or frame, surpassing basic object categorization, which only assigns a label to the entire object without specifying its precise position or contextual features. The purpose of this research is to search the use of the NVIDIA Jetson Nano terrace real-time object labelling on edge ploys. Accompanying applications varying from driverless cabs to following, object detection is an essential task in calculating apparition. The Jetson Nano is an excellent option for efficiently killing complex deep knowledge models in settings accompanying restricted money because of the allure of GPU-increased computational volume. In this chapter, we try the exercise of contemporary object detection models on the Jetson Nano, containing YOLO (You Only Look Once) and SSD (Single-Shot MultiBox Detector). Allowing for the possibility of the work-offs middle from two points model complicatedness and palpable-time conclusion, we evaluate these models’ veracity and speed of operation. We likewise scrutinize growth methods to further correct conduct accompanying minimal abeyance, like model quantization and ornamentation. Implementing object detection on edge devices like the Jetson Nano is both feasible and effective, as research advancements drive the development of embedded computing frameworks.