Our project is able to detect the objects whether it be the image, video or real time camera access. Fast and accurate object detection being our main focus, we were able to achieve it using YOLOv3 with more accurate results.

In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given class. The project titled ‘Face Detection and Recognition System’, is to manage all the front end back end system of finding or detecting particular region in human face.

In this study, we have devised a method to assist visually impaired people in detecting objects in their environment. We have proposed to create a web application that would detect the objects and various things in the surrounding within the help of certain machine learning techniques.

real-time YOLO-based object detection framework using OpenCV demonstrates the potential for accurate and efficient object detection in real-world scenarios, contributing to advancements in computer vision applications and paving the way for future

Aug 10, 2020 · Our aim is to classify objects using different image processing algorithms on the parameters like colour, shape and texture. The input for the system will be a video, which will be converted into frames and then processed for detecting the colour, shape or texture.

Deep learning-based object detection techniques, such as region-based CNNs (R-CNN), You Only Look Once (YOLO), and Single Shot MultiBox Detector (SSD), have become state-of-the-art methods for accurate and real-time object detection in various applications, such as autonomous

The model is trained to detect objects in real-time. This can be best achieved through a universal and open source library-TensorFlow (TF). Within the TF environment, multiple algorithms can be used for a wide range of datasets. In this paper, we have made use of the CIFAR-10 dataset, objects seen on a daily basis. 2. BASIC CNN COMPONENTS

Object detection methods aim to identify all target objects in the target image and determine the categories and position information in order to achieve machine vision understanding.

YOLOv3 (You Only Look Once, Version 3) is a real-time object detection algorithm that identifies specific objects in videos, live feeds, or images. YOLO uses features learned by a deep Convolutional neural network to detect an object.