A Method for Reducing False Negative Rate in Non-Maximum Suppression of YOLO Using Bounding Box Density
Dong-Hyeon Jeon, Tae-Sung Kim, Jin-Sung Kim · Journal of Multimedia Information System · 2023
In the previous non-maximum suppression (NMS) in you only look once (YOLO) v5, false negative error happens even when there are many bounding boxes for an object because all bounding boxes have lower confidence score. This work finds that a lot of bounding boxes near an object of false negative error are removed because of low confidence score. This paper proposes a new modified confidence score that is increased when bounding boxes with the same class prediction are located densely. The proposed method reduces the false negative error caused by low confidence score effectively. Experimental results show that the proposed method detects 25.33% more objects than conventional NMS at [email protected].