An Effective Interval Normalization Weighting Method for Accurate Object Detection

An effective method for improving the object detection performance is to decrease the number of false positive (NFP) detection boxes and increase the number of true positive (NTP) detection boxes. In terms of the region-based object detection framework, an appropriate sample weighting strategy can help effectively achieve this goal without causing any inference efficiency loss. However, designing a suitable weighting method is not easy, and a reasonable guiding metric and comprehensive analysis are needed. This article directly sets the NFP and NTP as the evaluation metrics and examines how some preliminary weighting methods affect these two metrics. Based on the results of our analysis, we carefully design a simple yet effective sample weighting method, referred to as the interval normalization weighting strategy (INWS). Unlike some previous works, which only view sample losses as the weighting factor (e.g., focal losses), the INWS applies both the foreground score and the intersection over union (IoU) as the weighting factors. The INWS consists of two components: the IoU interval score normalization strategy (IISNS) for negative samples and the score interval IoU normalization strategy (SIINS) for positive samples. The IISNS can effectively decrease the NFP, and the SIINS is beneficial for increasing the NTP, especially under higher IoU thresholds. Furthermore, the INWS is convenient for application to most of the existing region-based object detection models. The experimental results on the mainstream benchmarks demonstrate that our INWS can achieve consistent improvements on various baselines.