科研之友
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摘要
Non-maximum suppression (NMS) is widely adopted as a post-processing step in the state-of-the-art object detection pipelines to merge the nearby detections around one object. However, its performance is affected by objects that are highly overlapped with each other, and its localization accuracy depends solely on the highest scored detection. To tackle this, an accurate NMS method is proposed in this letter, which gradually merges the highly overlapped detections in an iterative way. In each iteration, detections overlapped with the highest scored one are grouped with a harder threshold to regress for a new proposal, and then the scores within the group are softly suppressed. This process is recursively applied on the remaining detections. The proposed method can not only detect more overlapped objects, but also achieve better object localization accuracy. Experimental results demonstrate that this simple and unsupervised method can gain obvious performance improvement on the majority of classes, compared with the state-of-the-art NMS methods.
- 出版日期2018
- 单位中国人民解放军国防科学技术大学
