Object Extraction and Detection Using U2-Net and YOLOv7

Abstract

Object extraction, detection and matting of background are constitute foundational pursuits in computer vision across diverse application domains. For serving this purpose we delves into the usage of U²-Net, a deep learning model, and YOLOv7, an advanced object detection framework. This attempt of investigation of YOLOv7, an advanced object recogni- tion framework, and U²-Net, a deep learning network with a focus on noticeable object extraction. In our study, U²-Net and YOLOv7 are trained on mentioned data-sets, with a focus on their unique contributions to object extraction and detection and matting. The primary focus of this study is the practical exami- nation of their application, with particular attention to their real-world implementation along with associ- ated challenges. Notably, we emphasize our intention to reinforce this effort by providing these models with domain-specific information through training on pertinent data-sets. We carefully examine how U²- Net and YOLOv7 could improve the object detection and recognition performance of our study. The main objective of this research is to boost imagination in applications involving computer vision by providing real-world examples of how these models might be used.