Detecting Objects With Precision: Polygonal Bounding Box-Based Object Detection

Source: Google Images

Object detection is a crucial task in computer vision that involves identifying and localizing objects of interest in an image or video. It is used in a wide range of applications, including surveillance, autonomous driving, robotics, and medical imaging. The goal of object detection is to not only detect the presence of objects but also accurately localize them in the image and label them with the correct category. In recent years, significant progress has been made in object detection algorithms, largely driven by advances in deep learning. Today, various techniques and models are available for object detection, including bounding box-based methods and polygonal object detection. In this blog post, we will focus on polygonal object detection and explore its advantages over traditional bounding box-based methods. In the later part of the blog, we will also train a deep-learning model to do the same.

Advantages of Polygonal Bounding Box-Based Object Detection

While traditional rectangular-based object detection algorithms have been successful in identifying objects in images, they have some limitations. One of the primary challenges with using rectangular bounding boxes is that they do not accurately capture the shapes of objects with irregular…