In the daily application of an iris-recognition-at-a-distance(IAAD)system,many ocular images of low quality are acquired.As the iris part of these images is often not qualified for the recognition requirements,the mor...In the daily application of an iris-recognition-at-a-distance(IAAD)system,many ocular images of low quality are acquired.As the iris part of these images is often not qualified for the recognition requirements,the more accessible periocular regions are a good complement for recognition.To further boost the performance of IAAD systems,a novel end-to-end framework for multi-modal ocular recognition is proposed.The proposed framework mainly consists of iris/periocular feature extraction and matching,unsupervised iris quality assessment,and a score-level adaptive weighted fusion strategy.First,ocular feature reconstruction(OFR)is proposed to sparsely reconstruct each probe image by high-quality gallery images based on proper feature maps.Next,a brand new unsupervised iris quality assessment method based on random multiscale embedding robustness is proposed.Different from the existing iris quality assess-ment methods,the quality of an iris image is measured by its robustness in the embedding space.At last,the fusion strategy exploits the iris quality score as the fusion weight to coalesce the complementary information from the iris and periocular regions.Extensive experi-mental results on ocular datasets prove that the proposed method is obviously better than unimodal biometrics,and the fusion strategy can significantly improve therecognition performance.展开更多
针对小尺寸列车风管与折角塞门的检测问题,提出一种多尺度特征增强的YOLOv5s检测算法。首先在主干网络Backbone增加了一个大尺寸特征图输出分支,使用C2f(CSP bottleneck with 2 convolution and forward)模块丰富新增特征图的梯度流信息...针对小尺寸列车风管与折角塞门的检测问题,提出一种多尺度特征增强的YOLOv5s检测算法。首先在主干网络Backbone增加了一个大尺寸特征图输出分支,使用C2f(CSP bottleneck with 2 convolution and forward)模块丰富新增特征图的梯度流信息;然后在Neck部分使用自适应空间特征融合增强特征金字塔模型FPN (Feature Pyramid Network)的多尺度特征融合能力,并使用路径聚合模型PAN(Path Aggregation Network)提高目标定位能力;检测头Head拥有4种尺度的特征图,其中新增特征图的尺寸最大,用于增强小尺寸目标的检测能力。实验结果表明,多尺度特征增强的YOLOv5s比原来的YOLOv5s在平均精度mAP(mean Average Precision)上提升了1.5%,优于其他检测器,提升了小尺寸风管和折角塞门的检测能力。展开更多
基金This work was supported by National Natural Science Foundation of China(Nos.62006225,61906199 and 62071468)the Strategic Priority Research Program of Chinese Academy of Sciences(CAS),China(No.XDA 27040700)sponsored by The Beijing Nova Program,China(Nos.Z201100006820050 and Z211100002121010).
文摘In the daily application of an iris-recognition-at-a-distance(IAAD)system,many ocular images of low quality are acquired.As the iris part of these images is often not qualified for the recognition requirements,the more accessible periocular regions are a good complement for recognition.To further boost the performance of IAAD systems,a novel end-to-end framework for multi-modal ocular recognition is proposed.The proposed framework mainly consists of iris/periocular feature extraction and matching,unsupervised iris quality assessment,and a score-level adaptive weighted fusion strategy.First,ocular feature reconstruction(OFR)is proposed to sparsely reconstruct each probe image by high-quality gallery images based on proper feature maps.Next,a brand new unsupervised iris quality assessment method based on random multiscale embedding robustness is proposed.Different from the existing iris quality assess-ment methods,the quality of an iris image is measured by its robustness in the embedding space.At last,the fusion strategy exploits the iris quality score as the fusion weight to coalesce the complementary information from the iris and periocular regions.Extensive experi-mental results on ocular datasets prove that the proposed method is obviously better than unimodal biometrics,and the fusion strategy can significantly improve therecognition performance.
文摘针对小尺寸列车风管与折角塞门的检测问题,提出一种多尺度特征增强的YOLOv5s检测算法。首先在主干网络Backbone增加了一个大尺寸特征图输出分支,使用C2f(CSP bottleneck with 2 convolution and forward)模块丰富新增特征图的梯度流信息;然后在Neck部分使用自适应空间特征融合增强特征金字塔模型FPN (Feature Pyramid Network)的多尺度特征融合能力,并使用路径聚合模型PAN(Path Aggregation Network)提高目标定位能力;检测头Head拥有4种尺度的特征图,其中新增特征图的尺寸最大,用于增强小尺寸目标的检测能力。实验结果表明,多尺度特征增强的YOLOv5s比原来的YOLOv5s在平均精度mAP(mean Average Precision)上提升了1.5%,优于其他检测器,提升了小尺寸风管和折角塞门的检测能力。
