BandRe: Rethinking Band-Pass Filters for Scale-Wise Object Detection Evaluation
This work addresses the need for more precise and reliable evaluation metrics for object detection in real-world applications, but it is incremental as it builds on existing evaluation frameworks.
The paper tackles the problem of evaluating object detectors across different scales by proposing new scale-wise metrics that balance fineness and reliability using triangular and trapezoidal band-pass filters, and shows these metrics can highlight differences between methods and datasets in experiments.
Scale-wise evaluation of object detectors is important for real-world applications. However, existing metrics are either coarse or not sufficiently reliable. In this paper, we propose novel scale-wise metrics that strike a balance between fineness and reliability, using a filter bank consisting of triangular and trapezoidal band-pass filters. We conduct experiments with two methods on two datasets and show that the proposed metrics can highlight the differences between the methods and between the datasets. Code is available at https://github.com/shinya7y/UniverseNet .