Improving Object Detection by Estimating Bounding Box Quality Accurately

Abstract

Object detection aims to locate and classify object instances in images. Therefore, the object detection model is generally implemented with two parallel branches to optimize localization and classification. After training the detection model, we should select the best bounding box of each class among a number of estimations for reliable inference. Generally, NMS (Non Maximum Suppression) is operated to suppress low-quality bounding boxes by referring to classification scores or center-ness scores. However, since the quality of bounding boxes is not considered, the low-quality bounding boxes can be accidentally selected as a positive bounding box for the corresponding class. We believe that this misalignment between two parallel tasks causes degrading of the object detection performance. In this paper, we propose a method to estimate bounding boxes' quality using four-directional Gaussian quality modeling, which leads the consistent results between two parallel branches. Extensive experiments on the MS COCO benchmark show that the proposed method consistently outperforms the baseline (FCOS). Eventually, our best model offers the state-of-the-art performance by achieving 48.9% in AP. We also confirm the efficiency of the method by comparing the number of parameters and computational overhead.