Object Detection with Convolutional Neural Networks
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Dec 2019.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1.
4 papers.
- 2021Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluationscited by 38
- 2021SGNet: A Super-class Guided Network for Image Classification and Object Detectioncited by 13
- 2022Dynamic Label Assignment for Object Detection by Combining Predicted IoUs and Anchor IoUscited by 6
- 2023Document AI: A Comparative Study of Transformer-Based, Graph-Based Models, and Convolutional Neural Networks For Document Layout Analysiscited by 0
- 2023Document AI: A Comparative Study of Transformer-Based, Graph-Based Models, and Convolutional Neural Networks For Document Layout Analysis29 Aug 2023
- 2022Dynamic Label Assignment for Object Detection by Combining Predicted IoUs and Anchor IoUs23 Jan 2022
- 2021SGNet: A Super-class Guided Network for Image Classification and Object Detection26 Apr 2021
- 2021Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations22 Apr 2021