# A Review of Object Detection Models based on Convolutional Neural Network

## TL;DR

This paper reviews CNN-based object detection models, categorizing them into two-stage and one-stage approaches, highlighting advancements from R-CNN to RefineDet, and comparing their architectures and training details.

## Contribution

It provides a comprehensive categorization and comparison of CNN-based object detection models, summarizing their development and training methodologies.

## Key findings

- Advancements from R-CNN to RefineDet are discussed.
- Models are categorized into two-stage and one-stage approaches.
- Comparison of model architectures and training details.

## Abstract

Convolutional Neural Network (CNN) has become the state-of-the-art for object detection in image task. In this chapter, we have explained different state-of-the-art CNN based object detection models. We have made this review with categorization those detection models according to two different approaches: two-stage approach and one-stage approach. Through this chapter, it has shown advancements in object detection models from R-CNN to latest RefineDet. It has also discussed the model description and training details of each model. Here, we have also drawn a comparison among those models.