一种边缘梯度插值的感兴趣区域池化算法  

A Region of Interest Pooling Algorithm forEdge Gradient Interpolation

作　　者：周跃进 丁家益 ZHOU Yuejin;DING Jiayi(School of Mathematics and Big Data,Anhui University of Science and Technology,Huainan 232001,Anhui Province,China;State Key Laboratory of Mining Response and Disaster Preventionand Control in Deep Coal Mines,Huainan 232001,Anhui Province,China)

机构地区：[1]安徽理工大学数学与大数据学院,安徽淮南232001 [2]深部煤矿采动响应与灾害防控国家重点实验室,安徽淮南232001

出　　处：《吉林大学学报（理学版）》2024年第3期643-654,共12页Journal of Jilin University:Science Edition

基　　金：国家自然科学基金(批准号:61703005);深部煤矿采动响应与灾害防控国家重点实验室基金(批准号:SKLMRDPC22KF03).

摘　　要：针对现有主流的目标检测算法存在检测精确率低、图像边缘区域分割不全等问题,提出一种基于Mask RCNN模型的感兴趣区域池化算法.首先,通过Otsu阈值分割法将感兴趣区域特征图划分为边缘区域和非边缘区域;其次,对边缘区域使用边缘梯度插值算法进行插值,对非边缘区域使用双线性插值算法进行插值,从而将离散的特征图映射到一个连续空间中;再次,将插值后的特征图均匀分割成k×k个单元;最后,对每个单元利用二重积分求均值以完成池化操作.对比实验结果表明,该算法基于Mask RCNN模型在数据集COCO(2014)上比现有算法的检测精确率有一定提升,对图像边缘区域的细节分割效果较好.Aiming at the problems that the existing mainstream target detection algorithms had low detection accuracy and incomplete segmentation in the image edge regions,we proposed a region of interest pooling algorithm based on Mask RCNN model.Firstly,the feature maps of the regions of interest were divided into edge regions and non-edge regions by the Otsu threshold segmentation method.Secondly,the edge gradient interpolation algorithm was used to interpolate for the edge regions,and the bilinear interpolation algorithm was used to interpolate for the non-edge regions so that the discrete feature map was mapped into a continuous space.Thirdly,the interpolated feature maps were evenly divided into k×k units.Finally,the double integral was used to calculate the average value of each unit to complete the pooling operation.The comparative experimental results show that the proposed algorithm,based on the Mask RCNN model,has a certain improvement in detection accuracy compared with existing algorithms on COCO(2014)dataset,and has a good segmentation effect on the details of the image edge regions.

关 键 词：Mask RCNN模型 感兴趣区域池化 Otsu阈值分割 边缘梯度插值 双线性插值 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]