Real-time object detection using the SSD MobileNet V3 architecture
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Updated
Sep 21, 2024 - Python
Real-time object detection using the SSD MobileNet V3 architecture
Object Detection using MobileNet SSD caffe model
MobileNetV3 SSD的简洁版本
Face detection model to classify either the face is spoof or not using MobileNet v2 SSD
In this project object detection is applied on real time drone using mobile net ssd.
This repository contains Python code for a project that performs American Sign Language (ASL) detection using multiclass classification. It utilizes YOLO (You Only Look Once) and MobileNetSSD_deploy for object detection, achieving an accuracy of 91%. The code offers options to predict signs from both images and videos.
Robot-Deployment Team Final Project: Trained Neural Network implementation for custom image recognition
Detecting licence plates using OpenALPR Library and finding the people without helmets using helmet detection program
Real Time Object Detection With MobileNet and SSD
This project is developed with Python and TensorFlow and is designed to detect sign language live. It uses computer vision techniques to capture the user's gestures in real-time and predict the corresponding sign language symbol.
Object detection with machine learning and OpenCV
Object detection at the edge, with Google's Coral dev board
Python script for object detection using Intel Movidius Neural Compute Stick and a pretrained model to be further used on Raspberry-based hexapod robot.
My playground of self-driving GoPiGo3 car using deep learning for computer vision. Technical details can be found in https://blog.zhijiahu.dev/
A Light CNN based Method for Hand Detection and Orientation Estimation
Real-time human detection, tracking and counting using MobileNet SSD
A collection of deep learning models (PyTorch implemtation)
A human detection, tracking and counting system built using deep learning based computer vision.
Desktop app for detecting highway lane violations. Illegal parking, Wrong direction, Illegal over taking can be detected
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.
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