  Previous single-stage detectors typically suffer the misalignment between
localization accuracy and classification confidence. To solve the misalignment
problem, we introduce a novel rectification method named neighbor IoU-voting
(NIV) strategy. Typically, classification and regression are treated as
separate branches, making it challenging to establish a connection between
them. Consequently, the classification confidence cannot accurately reflect the
regression quality. NIV strategy can serve as a bridge between classification
and regression branches by calculating two types of statistical data from the
regression output to correct the classification confidence. Furthermore, to
alleviate the imbalance of detection accuracy for complete objects with dense
points (easy objects) and incomplete objects with sparse points (difficult
objects), we propose a new data augmentation scheme named object resampling. It
undersamples easy objects and oversamples difficult objects by randomly
transforming part of easy objects into difficult objects. Finally, combining
the NIV strategy and object resampling augmentation, we design an efficient
single-stage detector termed NIV-SSD. Extensive experiments on several datasets
indicate the effectiveness of the NIV strategy and the competitive performance
of the NIV-SSD detector. The code will be available at
https://github.com/Say2L/NIV-SSD.
