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.
A final project which consists of object classification and detection based on the retraining of the Darknet YOLO neural network. Carried out by Alejandro Mendoza Barrionuevo, as part of his degree studies in Electronics, Robotics and Mechatronics Engineering for the University of Seville.
Vehicle Detection from Video Analytics.
Convolutional Neural Networks
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
Convolutional Neural Networks
A fork of darknet. Optimized and efficient deep learning framework. Better log, better model.
Pothole Detection and mapping using YoloV4, done for S6 Mini project
Fork. Darknet framework with modifications for easing the training and evaluating
This project combines YOLOv2 and seq-nms to realise real time video detection. Main contribution: creation of easy to follow insctructions.
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