You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
By using SSD, we only need to take one single shot to detect multiple objects within the image, while regional proposal network (RPN) based approaches such as R-CNN series that need two shots, one for generating region proposals, one for detecting the object of each proposal. Thus, SSD is much faster compared with two-shot RPN-based approaches.
Python script for object detection using Intel Movidius Neural Compute Stick and a pretrained model to be further used on Raspberry-based hexapod robot.
Object detection is detecting and recognizing the object. It is one of the common applications in computer vision problems (like traffic signals, people tracking, vehicle detection, etc...). In this repo, I develop real-time object detection with pre-trained models. These are YOLO version 3 and SSD MobileNet version 3. And I used coco large data…
To develop a prototype device for visually-impaired individuals so they can navigate their route through a known or unknown environment by providing data and cues of common objects or of a known person, that surrounds such individual in their day-to-day life, by making a stand-alone device requiring minimum and transportable equipment.
ODIN-AI or Object detection and identification ai is a flutter based mobile application that can detect up-to 9 objects in the camera and assign them with their predicted labels. The deep learning technique used is transfer learning using OpenCV and the SSD MobileNet model.
A web application which translates speech to sign language and sign language to speech in order to minimize the communication gap between normal people and the people with special need.