Citation

Li Z, Jia B, He Y, Xie L. Appl. Intell (Dordr.) 2022; 52(3): 2930-2941.

Copyright

(Copyright © 2022, Springer)

DOI

10.1007/s10489-021-02540-x

PMID

unavailable

Abstract

An inevitable of object detection is the existence of false positive detection boxes. The existence of a large number of false detection boxes greatly reduces the precision and compromises the desired effect. In this paper, we propose a post-processing method named Prediction Box Density Evaluation (PBDE). During applying object detect models to actual application scenarios, we summarize box density characteristics of true positive (TP) and false positive (FP) boxes. Then we set a threshold of box density to filter out a large number of FP boxes. After applying the PBDE algorithm, we obtain a significant improvement in precision and F1-Score. We have verified the effectiveness of our post-processing method in different application scenarios and models. The entire algorithm is carried out on the post-processing of object detection. There is no need to change the original training method and network structure, which is of great practicality and generality.

Language: en