计算机科学 ›› 2021, Vol. 48 ›› Issue (10): 233-238.doi: 10.11896/jsjkx.200900172
罗月童, 江佩峰, 段昶, 周波
LUO Yue-tong, JIANG Pei-feng, DUAN Chang, ZHOU Bo
摘要: 基于深度学习的目标检测算法广泛应用于工业检测,RetinaNet算法因兼具速度与精度两方面的优势而备受关注,但对于小于32×32像素的小目标,该算法的检测精度不能满足工业检测的要求。为此,文中以增强小目标的训练为基本思路,针对RetinaNet算法进行了如下改进:在采样阶段,将低层特征图P2添加到FPN中,以确保小目标能被充分采样,同时引入自适应训练样本选择策略,以保证增加特征层之后仍能保持足够快的检测速度;在训练后期采用了损失权重调整策略,用于提高小目标中困难样本的拟合度。针对公共数据集MS COCO 2017及实际应用中的LED点胶工业数据集,改进后的方法使小于32×32目标的检测精度分别提高了4.1%和10.7%,这表明改进后的方法能显著提升小目标检测的水平。
中图分类号:
[1]HU G X,YANG Z,HU L,et al.Small object detection withmultiscale features[J].International Journal of Digital Multimedia Broadcasting,2018(2):1-10. [2]GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2014:580-587. [3]GIRSHICK R.Fast r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:1440-1448. [4]REN S,HE K,GIRSHICK R,et al.Faster r-cnn:Towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems.2015:91-99. [5]UIJLINGS J R R,VAN DE SANDE K E A,GEVERS T,et al.Selective search for object recognition[J].International Journal of Computer Vision,2013,104(2):154-171. [6]REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:Unified,real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:779-788. [7]LIU W,ANGUELOV D,ERHAN D,et al.Ssd:Single shotmultibox detector[C]//European Conference on Computer Vision.Springer,Cham,2016:21-37. [8]LIN T Y,GOYAL P,GIRSHICK R,et al.Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:2980-2988. [9]SINGH B,DAVIS L S.An analysis of scale invariance in object detection snip[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:3578-3587. [10]SINGH B,NAJIBI M,DAVIS L S.SNIPER:Efficient multi-scale training[C]//Advances in Neural Information Processing Systems.2018:9310-9320. [11]HU P,RAMANAN D.Finding tiny faces[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:951-959. [12]LIU S,QI L,QIN H,et al.Path aggregation network for instance segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:8759-8768. [13]QIN Z,LI Z,ZHANG Z,et al.ThunderNet:Towards Real-Time Generic Object Detection on Mobile Devices[C]//Proceedings of the IEEE International Conference on Computer Vision.2019:6718-6727. [14]PANG J,CHEN K,SHI J,et al.Libra r-cnn:Towards balanced learning for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:821-830. [15]BAI Y,ZHANG Y,DING M,et al.Sod-mtgan:Small object detection via multi-task generative adversarial network[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:206-221. [16]LI J,LIANG X,WEI Y,et al.Perceptual generative adversarial networks for small object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:1222-1230. [17]NOH J,BAE W,LEE W,et al.Better to Follow,Follow to Be Better:Towards Precise Supervision of Feature Super-Resolution for Small Object Detection[C]//Proceedings of the IEEE International Conference on Computer Vision.2019:9725-9734. [18]ZHU C,TAO R,LUU K,et al.Seeing Small Faces from Robust Anchor's Perspective[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:5127-5136. [19]ZHANG S,CHI C,YAO Y,et al.Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection[J].arXiv:1912.02424,2019. [20]CHEN Y,ZHANG P,LI Z,et al.Stitcher:Feedback-driven Data Provider for Object Detection[J].arXiv:2004.12432,2020. [21]YU X,GONG Y,JIANG N,et al.Scale Match for Tiny Person Detection[C]//The IEEE Winter Conference on Applications of Computer Vision.2020:1257-1265. [22]KISANTAL M,WOJNA Z,MURAWSKI J,et al.Augmentation for small object detection[J].arXiv:1902.07296,2019. [23]LIN T Y,DOLLÁR P,GIRSHICK R,et al.Feature pyramidnetworks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:2117-2125. [24]HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778. [25]LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:3431-3440. [26]DUAN K,DU D,QI H,et al.Detecting small objects using a channel-aware deconvolutional network[C]//IEEE Transactions on Circuits and Systems for Video Technology.2019. [27]LI B,LIU Y,WANG X.Gradient harmonized single-stage detector[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2019,33:8577-8584. [28]CHEN K,WANG J,PANG J,et al.MMDetection:Open mmlab detection toolbox and benchmark[J].arXiv:1906.07155,2019. |
[1] | 徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺. 时序知识图谱表示学习 Temporal Knowledge Graph Representation Learning 计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204 |
[2] | 饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277 |
[3] | 汤凌韬, 王迪, 张鲁飞, 刘盛云. 基于安全多方计算和差分隐私的联邦学习方案 Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy 计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108 |
[4] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
[5] | 王剑, 彭雨琦, 赵宇斐, 杨健. 基于深度学习的社交网络舆情信息抽取方法综述 Survey of Social Network Public Opinion Information Extraction Based on Deep Learning 计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099 |
[6] | 郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077 |
[7] | 姜梦函, 李邵梅, 郑洪浩, 张建朋. 基于改进位置编码的谣言检测模型 Rumor Detection Model Based on Improved Position Embedding 计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046 |
[8] | 胡艳羽, 赵龙, 董祥军. 一种用于癌症分类的两阶段深度特征选择提取算法 Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification 计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092 |
[9] | 程成, 降爱莲. 基于多路径特征提取的实时语义分割方法 Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction 计算机科学, 2022, 49(7): 120-126. https://doi.org/10.11896/jsjkx.210500157 |
[10] | 侯钰涛, 阿布都克力木·阿布力孜, 哈里旦木·阿布都克里木. 中文预训练模型研究进展 Advances in Chinese Pre-training Models 计算机科学, 2022, 49(7): 148-163. https://doi.org/10.11896/jsjkx.211200018 |
[11] | 周慧, 施皓晨, 屠要峰, 黄圣君. 基于主动采样的深度鲁棒神经网络学习 Robust Deep Neural Network Learning Based on Active Sampling 计算机科学, 2022, 49(7): 164-169. https://doi.org/10.11896/jsjkx.210600044 |
[12] | 苏丹宁, 曹桂涛, 王燕楠, 王宏, 任赫. 小样本雷达辐射源识别的深度学习方法综述 Survey of Deep Learning for Radar Emitter Identification Based on Small Sample 计算机科学, 2022, 49(7): 226-235. https://doi.org/10.11896/jsjkx.210600138 |
[13] | 祝文韬, 兰先超, 罗唤霖, 岳彬, 汪洋. 改进Faster R-CNN的光学遥感飞机目标检测 Remote Sensing Aircraft Target Detection Based on Improved Faster R-CNN 计算机科学, 2022, 49(6A): 378-383. https://doi.org/10.11896/jsjkx.210300121 |
[14] | 王建明, 陈响育, 杨自忠, 史晨阳, 张宇航, 钱正坤. 不同数据增强方法对模型识别精度的影响 Influence of Different Data Augmentation Methods on Model Recognition Accuracy 计算机科学, 2022, 49(6A): 418-423. https://doi.org/10.11896/jsjkx.210700210 |
[15] | 毛典辉, 黄晖煜, 赵爽. 符合监管合规性的自动合成新闻检测方法研究 Study on Automatic Synthetic News Detection Method Complying with Regulatory Compliance 计算机科学, 2022, 49(6A): 523-530. https://doi.org/10.11896/jsjkx.210300083 |
|