Real-Time Object Detection Using SSD MobileNet Model of Machine Learning

Abstract

This research paper focuses on the application of computer vision techniques using Python and OpenCV for image analysis and interpretation. The main objective is to develop a system capable of performing various tasks such as object detection, recognition, and image processing. The project employs a combination of traditional computer vision algorithms and deep learning models to achieve accurate and efficient results. The research paper begins with essential preprocessing steps, including image acquisition, resizing, and noise reduction. Feature extraction techniques are utilized to capture relevant information from images, followed by object detection using methods like Haar cascades or deep learning-based approaches such as YOLO. Object recognition is achieved through feature matching or deep learning-based classification models. Furthermore, image processing techniques, including image enhancement, segmentation, and filtering, are applied to improve image quality and extract meaningful information. The system is implemented using Python programming language, leveraging the powerful OpenCV library for various computer vision tasks.

An Empirical Approach to Rerouting Visible Light Pathways Using an Adjustable-Angle Mirror to Sustain Communication between Vehicles on Curvy Roads

“…In our simulations, we approximate curvy roads by fitting a polynom The proportional controller [35,36] software used in the Arduino Uno (Arduino, Turin, Italy) analyzes the data obtained from sensors or other inputs and adjusts the output proportionally. In our experiment, the Arduino Uno was preferred due to its affordable price and accessibility.…”