Hausdorff IoU and Context Maximum Selection NMS: Improving Object Detection in Remote Sensing Images With a Novel Metric and Postprocessing Module

Abstract

The object detectors based on deep convolution neural network have achieved significant success in the field of remote sensing images. Intersection over Union (IoU) and No-maximum suppression (NMS) are the essential components of state-of-the-art anchor-based object detectors. However, as a localization evaluation metric, IoU does not precisely match the boundary box regression, leading to inaccurate regression of the object detector. Therefore, we introduce Hausdorff distance and combine it with IoU as a new evaluation metric (HIoU). NMS is an integral part of the object detection pipeline. However, it may lose relatively small object information in the case of high overlap. Because of the denseness of objects, this defect is more prominent in remote sensing image object detection. Therefore, we consider the context information of location confidence and propose the context maximum selection NMS (Cms-NMS) algorithm. Finally, we integrate HIoU and Cms-NMS into state-of-the-art object detectors, respectively. The performance of these object detectors is improved on the benchmark datasets NWPUVHR-10 and RSOD without any additional hyperparameters. The experiments show that HIoU and Cms-NMS are compatible, and using them together can further improve the detectors' accuracy.