# RGB-D image-based Object Detection: from Traditional Methods to Deep Learning Techniques

## TL;DR

This paper surveys the evolution of RGB-D object detection methods, from traditional hand-crafted features to modern deep learning techniques, highlighting their advancements, limitations, and future research directions.

## Contribution

It provides a comprehensive overview of recent developments in RGB-D object detection, emphasizing the transition from traditional to deep learning approaches.

## Key findings

- Deep learning has significantly improved detection performance.
- Traditional methods relied on hand-crafted features and machine learning.
- Deep learning approaches outperform earlier techniques in accuracy.

## Abstract

Object detection from RGB images is a long-standing problem in image processing and computer vision. It has applications in various domains including robotics, surveillance, human-computer interaction, and medical diagnosis. With the availability of low cost 3D scanners, a large number of RGB-D object detection approaches have been proposed in the past years. This chapter provides a comprehensive survey of the recent developments in this field. We structure the chapter into two parts; the focus of the first part is on techniques that are based on hand-crafted features combined with machine learning algorithms. The focus of the second part is on the more recent work, which is based on deep learning. Deep learning techniques, coupled with the availability of large training datasets, have now revolutionized the field of computer vision, including RGB-D object detection, achieving an unprecedented level of performance. We survey the key contributions, summarize the most commonly used pipelines, discuss their benefits and limitations, and highlight some important directions for future research.