Object Detection with Convolutional Neural Networks

TL;DR

This paper reviews recent advances in CNN-based object detection, comparing various models and addressing challenges like model degradation and small object detection to provide a comprehensive overview.

Contribution

It offers a summarized overview of CNN-based object detection models, highlighting architectural developments and problem-solving approaches.

Findings

Performance comparison of different models on benchmark datasets

Discussion of solutions for small-scale object detection

Analysis of model degradation issues in CNN detectors

Abstract

In this chapter, we present a brief overview of the recent development in object detection using convolutional neural networks (CNN). Several classical CNN-based detectors are presented. Some developments are based on the detector architectures, while others are focused on solving certain problems, like model degradation and small-scale object detection. The chapter also presents some performance comparison results of different models on several benchmark datasets. Through the discussion of these models, we hope to give readers a general idea about the developments of CNN-based object detection.