A real time soda can object detector using TensorFlow's Regions with Convolutional Neural Networks (R-CNN) adaptation integrated into Django web framework.

ORCA: Oceanic Recognition & Classification Application for sea-life analysis systems.

In this repository is my experimental thesis work on the recognition of museum works through object detection techniques.

A collection of computer vision projects, specifically covering image classification and object detection.

Implementation of Libra R-CNN: Towards Balanced Learning for Object Detection

Modifying pre-trained torchvision models.

Useful code for the users of the food flipping dataset, published in Nature Scientific Data.

Detecting Laryngeal Cancer from CT SCAN images using Improvised Deep Learning based Mask R-CNN Model

RCNN based Model Training

This project is centered around leveraging CRNN (Convolution Recurrent Neural Networks) and Digital Image Processing principles to extract license plates from car images and convert them to text. By using advanced AI algorithms and computer vision techniques, the project aims to provide a reliable and accurate way to recognize license plates.

Repo containing computer vision object detection work to locate bacterial flagellar motors from 2D cryogenic electromagnetic images.

An Object Detection project based on RCNN-Algorithm using Python

These days we all are using mask just because of Covid 19. So i build this website to detect whether your image is using mask or not by using Computer Vision and Deep Learning Algorithm to detect the image.

Mask R-CNN creates a high-quality segmentation mask in addition to the Faster R-CNN network. In addition to class labels and scores, a segmentation mask is created for the objects detected by this neural network. In this repository, using Anaconda prompt step by step Mask R-CNN setup is shown.

This work was part of detecting underground pipe detection using deep learning model called Faster R-CNN, was done as an Internship at CEREMA Laboratory, Angers, France. Supervised and guided by DAVID GUILBERT, Researcher and JAUFER RAKEEB, Phd student.

An object detection project implemented using Transfer Learning and R-CNN model. The object considered for this project is a "cellphone"

An IoT-based safety system utilising deep learning techniques to analyse environmental sounds in real-time. Employed sensors to capture audio signals and leveraged classification algorithms, including CNN-based and RNN-based models, for accurate sound recognition.

Object detection using R-CNN model from scratch.

Transforming road safety with advanced statistical analysis and modeling. Join us in revolutionizing driver assistance systems!

Exam Paper Analysis Project based on M-RCNN