Improved non-maximum suppression for detecting overlapping objects

Abstract

Non-maximum suppression (NMS) is widely used in object detectors for removing imprecise detection boxes. However, NMS can easily discard a part of correct detection boxes when multiple objects are overlapped. To deal with this problem, some methods had been presented, but only for simple overlapping scenes. Therefore, this paper proposes an improved NMS approach to detect objects with high degree of overlap. This method divides all of detection boxes into different clusters to reduce the degree of overlap between boxes. These detection box scores in each cluster are decayed as a function of overlap and no boxes are discarded. The improved NMS is combined with two commonly used object detection networks, namely Faster Region-based Convolutional Neural Networks and Region-based Fully Convolutional Networks. A complex public dataset Microsoft Common Objects in Context is employed to evaluate the performance of the improved NMS. Experimental results show that two metrics average recall and localization performance are improved by the proposed method for these two famous detectors.