Main idea: the realisation of a video object detector,on the basis of two methods YOLO and SEQ-NMS.
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Updated
Nov 28, 2022 - C
Main idea: the realisation of a video object detector,on the basis of two methods YOLO and SEQ-NMS.
Convolutional Neural Networks
On-board part of Project CAD2CAV: Computer Aided Design for Cooperative Autonomous Vehicles
This deep Learning model can detect the most of the objects on Indian roads.
A python wrapper to handle numpy arrays for YOLOV2.
Library for NJR4265RF2C1.
An object detector for processing videos, delivering quasi real-time results using CUDA.
Intelligent-surveillance-system aims to Detect the Object Detection, Crash Detection, Social Distance Detection and Fall Detection.
Enhancing Object Detection in using Thermal Imaging for thin cross-section unidentifiable objects(eg. cyclist, pedestrians).
[RA-L 2022] Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulations
The model is to detect the accident at the site and will identify the objects in terms of vehicle class whether the object is car, bus, van etc. and also detect the accident that is whether the accident happened or not. Proper labeling will be there which make sure to give whole information from the site we examine.This model will also run on vi…
This program demonstrates the usage of cosine similarity in designing an object detection algorithm
CPU Optimized & IoT Capable Embedded Computer Vision & Machine Learning Library.
Windows and Linux version of Darknet Yolo v3 & v2 Neural Networks for object detection (Tensor Cores are used)
Masks and FaceShield Detection for Covid 19
GM Autodrive Challenge Traffic Sign and Signal recognition 2021 utilizing Yolov4 architecture to identify relevant visual information for autonomous driving. Also contains the csp darknet source code as this is implemented as a ROS package. Relevant package code in /scripts and /launch primarily. Worked alongside Angelo Yang, Nicholas Vacek, Jun…
Implémentation of Yolov4 / Scaled-Yolov4 pour la détection d'objets haute performance à partir de réseaux de neurones profonds (forked)
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