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

Source: [An Improvement of Object Detection Performance using Multi-step Machine Learnings](<https://www.sota2.com/research/paper/68fa691c1e209131cea20520>)

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

Data updated: 2025-10-25T22:53:51.580Z

Authors: Tomoe Kishimoto, Masahiko Saito, Junichi Tanaka, Yutaro Iiyama, Ryu Sawada, Koji Terashi

Published: 2021-01-19T11:32:27.000Z

## 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.

## 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/2101.07571>)

[PDF](<https://arxiv.org/pdf/2101.07571.pdf>)