Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.

I have tested on Ubuntu 16.04/18.04. The code may work on other systems.

[Ubuntu-Deep-Learning-Environment-Setup]

• Ubuntu 16.04 / 18.04

• ROS Kinetic / Melodic

• GTX 1080Ti / RTX 2080Ti

• python 2.7 / 3.6

Clone the repository

git clone https://github.com/yehengchen/Object-Detection-and-Tracking.git

• How to train a YOLO model on custom images: YOLOv3 - [Here] / YOLOv4 - [Here]

• YOLOv4 + Deep_SORT - Pedestrian Counting & Social Distance - [Here]

• YOLOv3 + Deep_SORT - Pedestrian&Car Counting - [Here]

• YOLOv3 + SORT - Pedestrian Counting - [Here]

• YOLOv3 + ROS Kinetic - For small Custom Data - [Here]

• YOLOv3 + ROS Melodic - Robot Grasp Detection - [Here]

• Parts-Arrangement-Robot - [Here]

• YOLOv3 + OpenCV + ROS Melodic - Object Detection (Rotated) - [Here]

• DeepLab + OpenCV + ROS Melodic/Gazebo - Object Tracking - [Here]

• Mars_Rover + ROS Melodic/Gazebo - [Here]

Fast R-CNN / Faster R-CNN / Mask R-CNN

How to train a Mask R-CNN model on own images - [Here]

• Mask R-CNN + ROS Kinetic - [Here]

This project is ROS package of Mask R-CNN algorithm for object detection and segmentation.

• COCO dataset and Pascal VOC dataset - [Here]

• How to get it working on the COCO dataset coco2voc - [Here]

• Convert Dataset2Yolo - COCO / VOC - [Here]