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

• URL: http://arxiv.org/abs/2012.01201v1
• Date: Wed, 2 Dec 2020 13:36:51 GMT
• Title: Meta-Cognition-Based Simple And Effective Approach To Object Detection
• Authors: Sannidhi P Kumar, Chandan Gautam, Suresh Sundaram
• Abstract summary: We explore a meta-cognitive learning strategy for object detection to improve generalization ability while at the same time maintaining detection speed. The experimental results indicate an improvement in absolute precision of 2.6% (minimum), and 4.4% (maximum), with no overhead to inference time.
• Score: 4.68287703447406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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