# Meta-Cognition-Based Simple And Effective Approach To Object Detection

Source: [Meta-Cognition-Based Simple And Effective Approach To Object Detection](<https://www.sota2.com/research/paper/68fa68d51e209131cea1e78d>)

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

Data updated: 2025-10-25T22:46:52.640Z

Authors: Sannidhi P Kumar, Chandan Gautam, Suresh Sundaram

Published: 2020-12-02T13:36:51.000Z

## Abstract

Recently, many researchers have attempted to improve deep learning-based object detection models, both in terms of accuracy and operational speeds. However, frequently, there is a trade-off between speed and accuracy of such models, which encumbers their use in practical applications such as autonomous navigation. In this paper, we explore a meta-cognitive learning strategy for object detection to improve generalization ability while at the same time maintaining detection speed. The meta-cognitive method selectively samples the object instances in the training dataset to reduce overfitting. We use YOLO v3 Tiny as a base model for the work and evaluate the performance using the MS COCO dataset. The experimental results indicate an improvement in absolute precision of 2.6% (minimum), and 4.4% (maximum), with no overhead to inference time.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2012.01201>)

[PDF](<https://arxiv.org/pdf/2012.01201.pdf>)