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An Improvement of Object Detection Performance using Multi-step Machine Learnings

2021-01-19 11:32:27
Tomoe Kishimoto, Masahiko Saito, Junichi Tanaka, Yutaro Iiyama, Ryu Sawada, Koji Terashi
arXiv_CV
           
Abstract

Connecting multiple machine learning models into a pipeline is effective for handling complex problems. By breaking down the problem into steps, each tackled by a specific component model of the pipeline, the overall solution can be made accurate and explainable. This paper describes an enhancement of object detection based on this multi-step concept, where a post-processing step called the calibration model is introduced. The calibration model consists of a convolutional neural network, and utilizes rich contextual information based on the domain knowledge of the input. Improvements of object detection performance by 0.8-1.9 in average precision metric over existing object detectors have been observed using the new model.

Abstract (translated)

URL

https://arxiv.org/abs/2101.07571

PDF

https://arxiv.org/pdf/2101.07571.pdf

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