  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.
