基于深度学习的实例分割边界框回归方法研究OA北大核心CSTPCD

A new function of bounding box regression loss in the instance segmentation task is proposed in this paper to solve the problem of inaccurate bounding box location caused by occlusion or blurring of edges in an image.Here,the location prediction of a bounding box is transformed into the probability distribution of the estimated posi-tioning confidence changing with position.A method for area difference calculation is proposed considering the rela-tion between the coordinate points.Then,to prove that the method can be well applied in the instance segmentation model of the detection followed by segmentation,mask R-CNN is taken as the baseline.Experimental results reveal that the proposed method outperforms other methods in terms of accuracy in bounding box detection and instance segmentation.Furthermore,the effect is more significant for small object detection and segmentation,and the train-ing and evaluation speed is faster than others.