Object Detection Basics
Overview
Localizing objects in images accurately is an extremely useful technology which enables various applications for example regulating traffic.
This project provides a brief history of object detection and present the three most common algorithms in detail.
Topics covered
Some of the covered topics include:
Different goals of object recognition and object detection as one such goals
Datasets and their evolution for evaluating object recognition models
Explanation of evaluation metrics for object detection models, such as Average Precision, Average Recall on different scales, etc.
Introduction to detection before deep learning with definition of a feature in computer vision. Based on this, the traditional feature extractor models Scale-invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG) are explained.
An introduction to the convolutional neural network architecture
Detailed explanations of the Faster-R-CNN, You Only Look Once (YOLO), and The Detection Transformer (DETR) object detection model family architecture
Project links
References
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