# An Objectness Score for Accurate and Fast Detection during Navigation

arXiv: https://arxiv.org/abs/1909.05626 · HTML: https://mlsanity.com/paper/1909.05626/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [ShaSTA: Modeling Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking](https://mlsanity.com/paper/2211.03919/) (2022)

## Abstract (arXiv)

We propose a novel method utilizing an objectness score for maintaining the locations and classes of objects detected from Mask R-CNN during mobile robot navigation. The objectness score is defined to measure how well the detector identifies the locations and classes of objects during navigation. Specifically, it is designed to increase when there is sufficient distance between a detected object and the camera. During the navigation process, we transform the locations of objects in 3D world coordinates into 2D image coordinates through an affine projection and decide whether to retain the classes of detected objects using the objectness score. We conducted experiments to determine how well the locations and classes of detected objects are maintained at various angles and positions. Experimental results showed that our approach is efficient and robust, regardless of changing angles and distances.