# Object Detection with Convolutional Neural Networks

Source: [Object Detection with Convolutional Neural Networks](<https://www.sota2.com/research/paper/68fa66cc1e209131cea1267d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:58:21.378Z

Authors: Kaidong Li, Wenchi Ma, Usman Sajid, Yuanwei Wu, Guanghui Wang

Published: 2019-12-04T08:18:54.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1912.01844>)

[PDF](<https://arxiv.org/pdf/1912.01844.pdf>)