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.

提出了一种解决单阶段检测器中的定位准确性与分类置信度不匹配的问题的新方法，命名为邻居IoU投票（NIV）策略。通过计算来自回归输出的两种类型的统计数据来修正分类置信度，作为分类和回归分支之间的桥梁。并且，通过随机变换易对象的一部分为难对象，提出了一种名为对象重新采样的新数据增强方案，以减轻对完整对象和不完整对象的检测准确性不平衡的影响。结合NIV策略和对象重新采样增强，设计了一种高效的单阶段检测器NIV-SSD。在多个数据集上的广泛实验表明了NIV策略的有效性和NIV-SSD检测器的竞争性能。

NIV-SSD: 邻居IoU投票单阶段点云目标检测器