Aiming at the problem of missed detection and repeated detection in the single-threshold-non-maximum suppression algorithm

this paper proposes a dual-threshold Non-Maximum Suppression algorithm using GIoU (Generalized Intersection over Union). Using dual thresholds to improve the NMS algorithm and the soft-NMS algorithm

suppressing redundant detection boxes

not only balances the relationship between the object missed detection problem and the object false detection problem caused by the single threshold algorithm

but also reduces the occurrence of the soft-NMS algorithm. Based on the above

using GIoU instead of IoU to calculate the similarity between objects

so that the positioning of the object is more accurate; the non-linear function is used to give different weights to the proposal boxes

which makes the proposal boxes’ scores change non-linearly with distance

and the target discrimination is higher

which is more conducive to suppressing the proposal boxes. The detection accuracy of the improved algorithm on PASCAL VOC and MSCOCO is 74.8% and 25.9%

respectively. At the same time

the algorithm in this paper has a fast detection speed.