计算机科学 ›› 2020, Vol. 47 ›› Issue (11): 186-191.doi: 10.11896/jsjkx.191200063
樊玮1, 刘挺1, 黄睿1, 郭青2, 张宝2
FAN Wei1, LIU Ting1, HUANG Rui1, GUO Qing2, ZHANG Bao2
摘要： 流行的实例分割网络Mask R-CNN在进行实例分割时,存在目标分割边界和分割轮廓粗糙的问题,导致分割精度低。针对此问题,提出在Mask R-CNN分割分支中引入网络的低层卷积特征进行高精度的实例分割方法。首先从特征提取网络中选择特征,通过插值算法将其缩放至固定尺度(输入图像的1/8)作为低层特征;然后通过RoI对齐操作提取当前待分割目标的特征后与原始的Mask R-CNN的分割分支对应目标的特征进行拼接,并将其作为精细化目标分割的特征。低层网络特征引入了更多的低级纹理和轮廓信息,可以有效地提高物体的分割精度。在COCO2017数据集上,所提方法使用ResNet-101-FPN作为特征提取网络得到的分割结果的平均准确度(AP)相对于Mask R-CNN提高了1.2%。实验结果表明,所提方法在使用不同特征提取网络时具有较好的鲁棒性和有效性。
中图分类号:
| [1] LIU S,QI L,QIN H,et al.Path Aggregation Network for Instance Segmentation[J].arXiv:1803.01534. [2] LUO J,SAVAKIS A E,SINGHAL A.A Bayesian network-based framework for semantic image understanding[J].Pattern Recognition,2005,38(6):919-934. [3] LI L,JIANG S Q,HUANG Q M.Learning Hierarchical Semantic Description Via Mixed-Norm Regularization for Image Understanding[J].IEEE Transactions on Multimedia,2012,14(5):1401-1413. [4] LOZANO S,MÖLLER K,BRENDLE A,et al.AUTOPILOT-BT:A system for knowledge and model based mechanical ventilation[J].Technology and Health Care,2008,16(1):1-11. [5] THEIS J,OSSMANN D,THIELECKE F,et al.Robust autopilot design for landing a large civil aircraft in crosswind[J].Control Engineering Practice,2018,76:54-64. [6] ZHU J,LAO Y W,ZHENG Y F.Object Tracking in Structured Environments for Video Surveillance Applications[J].IEEE Transactions on Circuits and Systems for Video Technology,2010,20(2):223-235. [7] GILBERT A L,GILES M K,FLACHS G M,et al.A Real-Time Video Tracking System[J].IEEE Transactions on Pattern Ana-lysis and Machine Intelligence,1980(1):10. [8] SALTI S,CAVALLARO A,DI STEFANO L.Adaptive Ap-pearance Modeling for Video Tracking:Survey and Evaluation[J].IEEE Transactions on Image Processing,2012,21(10):4334-4348. [9] YEE K P,SWEARINGEN K,LI K,et al.Faceted metadata for image search and browsing[C]//Proceedings of the 2003 Conference on Human Factors in Computing Systems(CHI 2003).Ft.Lauderdale,Florida,USA,2003. [10] WANG M,LI H,TAO D C,et al.Multimodal Graph-BasedReranking for Web Image Search[J].IEEE Transactions on Ima-ge Processing,2012,21(11):4649-4661. [11] LI X,LIU Z,LUO P,et al.Not All Pixels Are Equal:Difficulty-aware Semantic Segmentation via Deep Layer Cascade[J].ar-Xiv:1704.01344. [12] LIU Z,LI X,LUO P,et al.Semantic Image Segmentation viaDeep Parsing Network[J].arXiv:1509.02634. [13] PINHEIRO P O,COLLOBERT R,DOLLAR P.Learning toSegment Object Candidates[J].arXiv:1506.06204. [14] PINHEIRO P O,LIN T Y,COLLOBERT R,et al.Learning to Refine Object Segments[J].arXiv:1603.08695. [15] DAI J,HE K,LI Y,et al.Instance-sensitive Fully Convolutional Networks[J].arXiv:1603.08678. [16] DAI J,HE K,SUN J.Instance-aware Semantic Segmentation via Multi-task Network Cascades[J].arXiv:1512.04412. [17] HAYDER Z,HE X,SALZMANN M.Boundary-Aware Instance Segmentation[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE,2017. [18] DAI J,LI Y,HE K,et al.R-FCN:Object Detection via Region-based Fully Convolutional Networks[J].arXiv:1605.06409. [19] GIRSHICK R.Fast r-cnn[C]//2015 IEEE International Confe-rence on Computer Vision (ICCV).IEEE,2016. [20] REN S,HE K,GIRSHICK R,et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,39(6):1137-1149. [21] LI Y,QI H,DAI J,et al.Fully Convolutional Instance-Aware Semantic Segmentation[C]//2017 IEEE Conference on Compu-ter Vision and Pattern Recognition (CVPR).Honolulu,HI:IEEE,2017:4438-4446. [22] CHEN L C,HERMANS A,PAPANDREOU G,et al.Mask-Lab:Instance Segmentation by Refining Object Detection with Semantic and Direction Features[J].arXiv:1712.04837. [23] HE K,GKIOXARI G,PIOTR DOLLÁ R,et al.Mask R-CNN[C]//2017 IEEE International Conference on Computer Vision (ICCV).IEEE,2017. [24] HUANG Z,HUANG L,GONG Y,et al.Mask Scoring R-CNN[J].arXiv:1903.00241. [25] CHEN K,PANG J,WANG J,et al.Hybrid Task Cascade forInstance Segmentation[J].arXiv:1901.07518. [26] CAI Z,VASCONCELOS N.Cascade R-CNN:Delving into High Quality Object Detection[J].arXiv:1712.00726. [27] SUN Y,P P S K,SHIMAMURA J,et al.Concatenated Feature Pyramid Network for Instance Segmentation[J].arXiv:1904.00768. [28] LIN T Y,DOLLÁR P,GIRSHICK R,et al.Feature PyramidNetworks for Object Detection[J].arXiv:1612.03144. [29] LIANG X,LIN L,WEI Y,et al.Proposal-free Network for Instance-level Object Segmentation[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2018,40(12):2978-2991. [30] BAI M,URTASUN R.Deep Watershed Transform for Instance Segmentation[J].arXiv:1611.08303. [31] BEUCHER S,C ANTUÉL.Use of Watersheds in Contour Detection[C]//International workshop on image processing,real-time edge and motion detection.CCETT,1979. [32] KIRILLOV A,LEVINKOV E,ANDRES B,et al.InstanceCut:from Edges to Instances with MultiCut[J].arXiv:1611.08272. [33] JIN L,CHEN Z,TU Z.Object Detection Free Instance Segmentation With Labeling Transformations[J].arXiv:1611.08991. [34] LIU S,JIA J,FIDLER S,et al.SGN:Sequential Grouping Networks for Instance Segmentation[C]//2017 IEEE International Conference on Computer Vision (ICCV).Venice:IEEE,2017:3516-3524. [35] REN M,ZEMEL R S.End-to-End Instance Segmentation with Recurrent Attention[C]//Computer Vision & Pattern Recognition.IEEE,2017. [36] ROMERA-PAREDES B,TORR P H S.Recurrent Instance Segmentation[J].Computer Science,2016,9910(10):312-329. [37] HOCHREITER S,SCHMIDHUBER J.Long Short-Term Memory[J].Neural Computation,1997,9(8):1735-1780. [38] SHI X,CHEN Z,WANG H,et al.Convolutional LSTM Net-work:A Machine Learning Approach for Precipitation Nowcas-ting[J].arXiv:1506.04214. [39] ZEILER M D,FERGUS R.Visualizing and Understanding Convolutional Networks[J].arXiv:1311.2901. [40] LIN T Y,MAIRE M,BELONGIE S,et al.Microsoft COCO:Common Objects in Context[C]//European Conference on Computer Vision.Springer International Publishing,2014. [41] MASSA F,GIRSHICK R.maskrcnn-benchmark:Fast,modularreference implementation of Instance Segmentation and Object Detection algorithms in PyTorch[OL].https://github.com/facebookresearch/maskrcnn-benchmark. |
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