计算机科学 ›› 2021, Vol. 48 ›› Issue (1): 175-181.doi: 10.11896/jsjkx.200200023
何彦辉1, 吴桂兴1,2, 吴志强1
HE Yan-hui1, WU Gui-xing1,2, WU Zhi-qiang1
摘要: 随着卷积神经网络的发展,X光安全检查图像的自动目标检测算法已经取得了重大进步。但是,当将这些目标检测算法应用到不同于训练集数据的新数据,即训练域数据和测试域数据的图像数据服从不一致的分布时,这些检测算法的性能通常会降低。根据X光成像的变化,提出一种基于上下文的透射率自适应域对齐方法,用于解决检测算法的域不适应问题。首先,通过利用X光图像中存在的颜色信息,设计了一种注意力机制来分别处理X光图像的每个颜色通道特征,解决不同X光机器的颜色差异问题。接着,提出一种多分辨率特征对齐方法,以解决不同厂商不同X光图像之间的数据分布差异。最后,使用上下文向量作为对抗训练的正则化,利用邻域信息提高测试精度。基于X光图像数据集和Cityscape数据集的实验表明,所提方法解决了目标检测算法在不同于训练域数据的测试域中精度下降的问题。
中图分类号:
[1] EVERINGHAM M,VAN GOOL L,WILLIAMS C K,et al.The pascal visual object classes(voc) challenge [J].IJCV 2010,88(2):303-338. [2] REN S,HE K,GIRSHICK R,et al.Faster rcnn:Towards real-time object detection with region proposal networks[C]//NIPS.2015:1-9. [3] MERY D,RIFFO V,ZSCHERPEL U,et al.Gdxray:The database of x-ray images for nondestructive testing[J].Journal of Nondestructive Evaluation,2010,34(4):42. [4] MIAO C J,XIE L X,WAN F,et al.SIXray:A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images[J].arXiv:1901.00303. [5] Ailibaba.Dataset :Logistics goods restriction monitoring[EB/OL].(2019-03-05) [2019-06-08].https://tianchi.aliyun.com/competition/entrance/231703/introduction. [6] GANIN Y,LEMPITSKY V.Unsupervised domain adaptationby backpropagation[C]//ICML.2014. [7] BEN-DAVID S,BLITZER J,CRAMMER K,et al.A theory of learning from different domains[J].Machine Learning,2010,79(1):151-175. [8] TZENG E,HOFFMAN J,SAENKO K,et al.Adversarial dis-criminative domain adaptation[C]//CVPR.2017:11,19. [9] TZENG E,HOFFMAN J,ZHANG N,et al.Deep domain confusion:Maximizing for domain invariance[J].arXiv:1412.3474. [10] LONG M,CAO Y,WANG J,et al.Learning transferable fea-tures with deep adaptation networks[C]//ICML.2015. [11] DUAN L,TSANG I W,XU D.Domain transfer multiple kernel learning[J].TPAMI,2012,34(3):465-479. [12] INOUE N,FURUTA R,YAMASAKI T,et al.Cross-domainweakly-supervised object detection through progressive domain adaptation[J].arXiv:1803.11365. [13] ZHU J Y,PARK T,ISOLA P,et al.Unpaired image-to-image translation using cycle-consistent adversarial networks[J].arXiv:1703.10593. [14] CHEN Y,LI W,SAKARIDIS C,et al.Domain adaptive faster r-cnn for object detection in the wild[J].arXiv:1803.03243. [15] SAITO K,USHIKU Y,HARADA T,et al.Strong-Weak Distribution Alignment for Adaptive Object Detection[C]//CVPR.2019:2154-2162. [16] ZHU X G,JIANG M,YANG C Y.Adapting Object Detectors via Selective Cross-Domain Alignment [C]//CVPR.2019:687-696. [17] SCHWAB P,KELLER E,MUROI C.Not to Cry Wolf:Distantly Supervised Multitask Learning in Critical Care[J].arXiv:1802.05027. [18] CAI Z,VASCONCELOS N.Cascade R-CNN:Delving into high quality object detection[J].arXiv:1712.00726. [19] HE K M,ZHANG X Y,REN S Q,et al.Deep Residual Learning for Image Recognition[J].arXiv:1512.03385. [20] LIN T Y,GOYAL P,GIRSHICK R,et al.Focal loss for dense object detection[J].arXiv:1708.02002. [21] LIN T Y,DOLLAR P,GIRSHICK R,et al.Feature pyramidnetworks for object detection[C]//CVPR.2017:2117-2125. [22] DENG J,DONG W,SOCHER R,et al.Imagenet:A large-scale hierarchical image database[C]//CVPR.2009:248-255. [23] CORDTS M,OMRAN M,RAMOS S,et al.The Cityscapesdataset for semantic urban scene understanding[C]//CVPR.2016:268-276. |
[1] | 周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026 |
[2] | 李宗民, 张玉鹏, 刘玉杰, 李华. 基于可变形图卷积的点云表征学习 Deformable Graph Convolutional Networks Based Point Cloud Representation Learning 计算机科学, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023 |
[3] | 陈泳全, 姜瑛. 基于卷积神经网络的APP用户行为分析方法 Analysis Method of APP User Behavior Based on Convolutional Neural Network 计算机科学, 2022, 49(8): 78-85. https://doi.org/10.11896/jsjkx.210700121 |
[4] | 朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153 |
[5] | 刘冬梅, 徐洋, 吴泽彬, 刘倩, 宋斌, 韦志辉. 基于边框距离度量的增量目标检测方法 Incremental Object Detection Method Based on Border Distance Measurement 计算机科学, 2022, 49(8): 136-142. https://doi.org/10.11896/jsjkx.220100132 |
[6] | 王灿, 刘永坚, 解庆, 马艳春. 基于软标签和样本权重优化的Anchor Free目标检测算法 Anchor Free Object Detection Algorithm Based on Soft Label and Sample Weight Optimization 计算机科学, 2022, 49(8): 157-164. https://doi.org/10.11896/jsjkx.210600240 |
[7] | 檀莹莹, 王俊丽, 张超波. 基于图卷积神经网络的文本分类方法研究综述 Review of Text Classification Methods Based on Graph Convolutional Network 计算机科学, 2022, 49(8): 205-216. https://doi.org/10.11896/jsjkx.210800064 |
[8] | 金方焱, 王秀利. 融合RACNN和BiLSTM的金融领域事件隐式因果关系抽取 Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM 计算机科学, 2022, 49(7): 179-186. https://doi.org/10.11896/jsjkx.210500190 |
[9] | 张颖涛, 张杰, 张睿, 张文强. 全局信息引导的真实图像风格迁移 Photorealistic Style Transfer Guided by Global Information 计算机科学, 2022, 49(7): 100-105. https://doi.org/10.11896/jsjkx.210600036 |
[10] | 戴朝霞, 李锦欣, 张向东, 徐旭, 梅林, 张亮. 基于DNGAN的磁共振图像超分辨率重建算法 Super-resolution Reconstruction of MRI Based on DNGAN 计算机科学, 2022, 49(7): 113-119. https://doi.org/10.11896/jsjkx.210600105 |
[11] | 刘月红, 牛少华, 神显豪. 基于卷积神经网络的虚拟现实视频帧内预测编码 Virtual Reality Video Intraframe Prediction Coding Based on Convolutional Neural Network 计算机科学, 2022, 49(7): 127-131. https://doi.org/10.11896/jsjkx.211100179 |
[12] | 徐鸣珂, 张帆. Head Fusion:一种提高语音情绪识别的准确性和鲁棒性的方法 Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition 计算机科学, 2022, 49(7): 132-141. https://doi.org/10.11896/jsjkx.210100085 |
[13] | 孙福权, 崔志清, 邹彭, 张琨. 基于多尺度特征的脑肿瘤分割算法 Brain Tumor Segmentation Algorithm Based on Multi-scale Features 计算机科学, 2022, 49(6A): 12-16. https://doi.org/10.11896/jsjkx.210700217 |
[14] | 吴子斌, 闫巧. 基于动量的映射式梯度下降算法 Projected Gradient Descent Algorithm with Momentum 计算机科学, 2022, 49(6A): 178-183. https://doi.org/10.11896/jsjkx.210500039 |
[15] | 杨涵, 万游, 蔡洁萱, 方铭宇, 吴卓超, 金扬, 钱伟行. 基于步态分类辅助的虚拟IMU的行人导航方法 Pedestrian Navigation Method Based on Virtual Inertial Measurement Unit Assisted by GaitClassification 计算机科学, 2022, 49(6A): 759-763. https://doi.org/10.11896/jsjkx.211200148 |
|