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

• URL: http://arxiv.org/abs/2101.07571v1
• Date: Tue, 19 Jan 2021 11:32:27 GMT
• Title: An Improvement of Object Detection Performance using Multi-step Machine Learnings
• Authors: Tomoe Kishimoto, Masahiko Saito, Junichi Tanaka, Yutaro Iiyama, Ryu Sawada and Koji Terashi
• Abstract summary: This paper describes an enhancement of object detection based on a 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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