电信科学 ›› 2020, Vol. 36 ›› Issue (7): 92-106.doi: 10.11959/j.issn.1000-0801.2020199
张婷婷1,章坚武1(),郭春生1,陈华华1,周迪2,王延松3,徐爱华2
修回日期:
2020-06-30
出版日期:
2020-07-20
发布日期:
2020-07-28
作者简介:
张婷婷(1995- ),女,杭州电子科技大学通信工程学院硕士生,主要研究方向为计算机视觉与人工智能等|章坚武(1961- ),男,博士,杭州电子科技大学通信工程学院教授、博士生导师,中国电子学会高级会员,浙江省通信学会常务理事,主要研究方向为移动通信、多媒体信号处理与人工智能、通信网络与信息安全|郭春生(1971- ),男,博士,杭州电子科技大学通信工程学院副教授、硕士生导师,主要研究方向为视频分析与模式识别|陈华华(1975- ),男,博士,杭州电子科技大学通信工程学院副教授、硕士生导师,主要研究方向为视频分析与模式识别|周迪(1975- ),男 ,浙江宇视科技有限公司教授级高级工程师、宇视研究院院长,主要研究方向为视频安全、人工智能等|王延松(1970- ),男 ,之江实验室研究员,教授级高工,科技部“宽带通信与新型网络”领域总体组专家、指南编制组专家,工信部“网络通信技术”领域咨询专家、中国通信学会委员、中国通信标准化协会工业互联网ST8组副组长等职务。主要研究方向为工业互联网、SDN/NFV、网络安全等|徐爱华(1989- ),女,浙江宇视科技有限公司工程师,主要研究方向为视频安全、人工智能等
基金资助:
Tingting ZHANG1,Jianwu ZHANG1(),Chunsheng GUO1,Huahua CHEN1,Di ZHOU2,Yansong WANG3,Aihua XU2
Revised:
2020-06-30
Online:
2020-07-20
Published:
2020-07-28
Supported by:
摘要:
图像目标检测是找出图像中感兴趣的目标,并确定他们的类别和位置,是当前计算机视觉领域的研究热点。近年来,由于深度学习在图像分类方面的准确度明显提高,基于深度学习的图像目标检测模型逐渐成为主流。首先介绍了图像目标检测模型中常用的卷积神经网络;然后,重点从候选区域、回归和anchor-free方法的角度对现有经典的图像目标检测模型进行综述;最后,根据在公共数据集上的检测结果分析模型的优势和缺点,总结了图像目标检测研究中存在的问题并对未来发展做出展望。
中图分类号:
张婷婷,章坚武,郭春生,陈华华,周迪,王延松,徐爱华. 基于深度学习的图像目标检测算法综述[J]. 电信科学, 2020, 36(7): 92-106.
Tingting ZHANG,Jianwu ZHANG,Chunsheng GUO,Huahua CHEN,Di ZHOU,Yansong WANG,Aihua XU. A survey of image object detection algorithm based on deep learning[J]. Telecommunications Science, 2020, 36(7): 92-106.
表1
基于深度学习的图像目标检测模型创新点及优缺点对比"
检测方法 | 模型 | 创新点 | 优缺点 |
基于候选区域 | R-CNN[ | 用CNN提取特征 | 用卷积网络提取特征;但特征提取复杂,耗时长 |
SPP-Net[ | 输入整张图片提取特征,并共享特征图 | 实现多尺度卷积计算;但占用较大硬件资源 | |
fast R-CNN[ | 用ROI pooling层提取特征 | 初步实现端对端检测;但依赖传统方法生成候选区域 | |
faster R-CNN[ | 提出区域生成网络(RPN) | 实现实时检测;但对小目标检测效果不佳 | |
mask R-CNN[ | 增加了用于分割任务的分支 | 减弱类别间的竞争优势;但与faster R-CNN相比,检测速度较慢 | |
基于回归 | ?OLO[ | 将图像空间划分为网格单元 | 检测速度快;但定位不准确,对密集物体检测效果不佳 |
?OLOv2[ | 使用聚类产生锚框 | 减少定位错误,提高分类精度;但准确率还不够高 | |
?OLO9000[ | 提出了目标分类和检测的联合训练方法 | 为跨数据集训练提供思路;但准确率还不够高 | |
?OLOv3[ | 借鉴残差学习思想,并进行多尺度检测 | 检测精度高并且速度是其他模型的3~4倍;但对边界框预测严格的情况下检测精度略低 | |
SSD[ | 生成多尺度的锚框对边界框空间离散化 | 检测速度快;但准确率低,对小目标检测效果不佳 | |
DSSD[ | 将ResNet-101作为骨干网,并添加反卷积层 | 提高小目标检测效果;但与SSD相比检测速度较慢 | |
RSSD[ | 改进特征融合方式 | 检测精度提高;但与SSD相比检测速度较慢 | |
FSSD[ | 重构金字塔特征图以融合不同尺度特征 | 有利于小目标检测;但与SSD相比检测速度较慢 | |
ASSD[ | 融合了注意力机制的思想 | 检测精度提高;但与SSD相比检测速度较慢 | |
RetinaNet[ | 重塑交叉熵损失,用焦点损失解决类不平衡问题 | 检测精度提高;但检测速度一般 | |
基 于anchor-free | CornerNet[ | 将物体检测为两个角点,无须生成锚框 | 检测精度提高;但容易产生错误目标框,检测速度一般 |
ExtremeNet[ | 通过预测极值点和中心点来检测目标 | 于 CornerNet 相比,对小目标检测效果更好;但对大目标检测效果略差 | |
CenterNet[ | 将物体检测为两个角点和一个中心点 | 解决容易产生错误目标框问题;但检测速度一般 | |
CenterNet[ | 直接判断像素点是否为目标的中心点 | 算法更简洁;但与同名CenterNet相比,检测精度有所降低 | |
FSAF[ | 提出动态选择特征的方式 | 摒弃了传统基于锚框选择合适特征层;但在anchor-free分支中引入了较多超参数 | |
FCOS[ | 针对每个像素点进行预测 | 可以与其他视觉任务相结合;但在检测时由于真实框重叠,可能会出现语义模糊情况 | |
FoveaBox[ | 对真实框进行位置变换,增加正负样本的识别度 | 有效地解决了正负样本不平衡问题;与其他anchor-free模型相比,检测精度略低 |
表2
基于深度学习的图像目标检测模型性能对比"
检测方法 | 模型 | 骨干网 | 2007 | 2012 | MS COCO | FPS | |
AP@0.5 | AP@[0.5,0.95] | ||||||
基于候选区域 | R-CNN[ | AlexNet[ | 58.5% | 53.3% | — | — | 0.02 |
SPP-Net[ | ZF-5[ | 59.2% | — | — | — | 0.5 | |
fast R-CNN[ | VGG-16[ | 66.9% | 66.0% | 35.9% | 19.7% | 0.5 | |
faster R-CNN[ | VGG-16 | 73.2% | 70.4% | 42.7% | 21.9% | 7 | |
mask R-CNN[ | ResNet-101-FPN[ | — | — | 58.0% | 35.7% | 5 | |
基于回归 | ?OLO[ | VGG-16 | 66.4% | 57.9% | — | — | 45 |
?OLOv2[ | DarkNet-19[ | 78.6% | 73.5% | 44.0% | 21.6% | 40 | |
?OLOv3[ | DarkNet-53[ | — | — | 57.9% | 33.0% | 19.6 | |
SSD[ | VGG-16 | 79.8% | 78.5% | 48.5% | 28.8% | 19 | |
DSSD[ | ResNet-101[ | 81.5% | 80.0% | 53.3% | 33.2% | 5.5 | |
RSSD[ | VGG-16 | 80.8% | — | — | — | 16.6 | |
FSSD[ | VGG-16 | 80.9% | 84.2% | 52.8% | 31.8% | 35.7 | |
ASSD[ | ResNet-101 | 83% | 81.3% | 55.5% | 34.5% | 2.7 | |
RetinaNet[ | ResNet-101-FPN | — | — | 59.1% | 39.1% | 8.2 | |
基于anchorfree | CornerNet[ | Hourglass[ | — | — | 57.8% | 42.1% | 4.1 |
ExtremeNet[ | Hourglass | — | — | 60.5% | 43.7% | 3.1 | |
CenterNet[ | Hourglass | — | — | 64.5% | 47.0% | 3.7 | |
CenterNet[ | Hourglass | — | — | 63.9% | 45.1% | 7.8 | |
FSAF[ | ResNet-101 | — | — | 63.1% | 42.8% | — | |
FCOS[ | ResNet-101-FPN | — | — | 60.7% | 41.5% | — | |
FoveaBox[ | ResNet-101 | — | — | 60.1% | 40.6% | — |
[1] | 刘芳, 杨安喆, 吴志威 . 基于自适应 Siamese 网络的无人机目标跟踪算法[J]. 航空学报, 2020,41(1): 248-260. |
LIU F , YANG A Z , WU Z W . Adaptive siamese network based UAV target tracking algorithm[J]. Acta Aeronautica et Astronautica Sinica, 2020,41(1): 248-260. | |
[2] | 陈莹莹, 房胜, 李哲 . 加权多特征外观表示的实时目标追踪[J]. 中国图象图形学报, 2019,24(2): 291-301. |
CHEN Y Y , FANG S , LI Z . Real-time visual tracking via weighted multi-feature fusion on an appearance model[J]. Journal of Image and Graphics, 2019,24(2): 291-301. | |
[3] | 何冰倩, 魏维, 张斌 . 基于深度学习的轻量型的人体动作识别模型[J]. 计算机应用研究, 2020,37(8): 1-6. |
HE B Q , WEI W , ZHANG B . Lightweight human action recognition model based on deep learning[J]. Application Research of Computers, 2020,37(8): 1-6. | |
[4] | 罗会兰, 童康 . 时空压缩激励残差乘法网络的视频动作识别[J]. 通信学报, 2019,40(10): 189-198. |
LUO H L , TONG K . Spatiotemporal squeeze-and-excitation residual multiplier network for video action recognition[J]. Journal on Communications, 2019,40(10): 189-198. | |
[5] | UIJLINGS J , VAN D S K , GEVERS T ,et al. Selective search for object recognition[J]. International Journal of Computer Vision, 2013,104(2): 154-171. |
[6] | GIRSHICK R , DONAHUE J , DARRELL T ,et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]// Proceedings of 27th IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2014: 580-587. |
[7] | 苏赋, 吕沁, 罗仁泽 . 基于深度学习的图像分类研究综述[J]. 电信科学, 2019,35(11): 58-74. |
SU F , LV Q , LUO R Z . Review of image classification based on deep learning[J]. Telecommunications Science, 2019,35(11): 58-74. | |
[8] | LECUN Y , BOTTOU L , BENGIO Y ,et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998,86(11): 2278-2324. |
[9] | KRIZHEVSKY A , SUTSKEVER I , HINTON G . ImageNet classification with deep convolutional neural networks[J]. Advances in Neural Information Processing Systems, 2012,25(2): 1097-1105. |
[10] | ZEILER M D , FERGUS R . Visualizing and understanding convolutional networks[C]// Proceedings of 13th European Conference on Computer Vision. Berlin:Springer-Verlag, 2014: 818-833. |
[11] | SIMONYAN K , ZISSERMAN A . Very deep convolutional networks for large-scale image recognition[C]// Proceedings of 3rd International Conference on Learning Representations.[S.l.:s.n]. 2015. |
[12] | LUO W J , LI Y J , URTASUN R ,et al. Understanding the effective receptive field in deep convolutional neural networks[J]. arXiv:1701.04128, 2017 |
[13] | HE K , ZHANG X , REN S ,et al. Deep residual learning for image recognition[C]// Proceedings of 29th IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2015: 770-778. |
[14] | SQUARTINI S , PAOLINELLI S , PIAZZA F . Comparing different recurrent neural architectures on a specific task from vanishing gradient effect perspective[C]// Proceedings of 2006 IEEE International Conference on Networking,Sensing and Control. Piscataway:IEEE Press, 2006: 380-385. |
[15] | PASCANU R , MIKOLOV T , BENGIO Y . Understanding the exploding gradient problem[J]. arXiv:1211.5063, 2012 |
[16] | NEWELL A , YANG K , DENG J . Stacked hourglass networks for human pose estimation[C]// Proceedings of 21st ACM Conference on Computer and Communications Security. Berlin:Springer-Verlag, 2016: 483-499. |
[17] | EVERINGHAM M , GOOL L V , WILLIAMS C K I ,et al. The pascal visual object classes (VOC) challenge[J]. International Journal of Computer Vision, 2010: 3485-3492. |
[18] | LIN T Y , MAIRE M , BELONGIE S ,et al. Microsoft COCO:common objects in context[C]// Proceedings of 13th European Conference on Computer Vision. Berlin:Springer-Verlag, 2014: 740-755. |
[19] | DONAHUE J , JIA Y , VINYALS O ,et al. DeCAF:a deep convolutional activation feature for generic visual recognition[C]// Proceedings of 31st International Conference on Machine Learning. New York:ACM Press, 2014: 988-996. |
[20] | BODLA N , SINGH B , CHELLAPPA R ,et al. Soft-NMS—improving object detection with one line of code[C]// Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV). Piscataway:IEEE Press, 2017: 5562-5570. |
[21] | HE K , ZHANG X , REN S ,et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014,37(9): 1904-1916. |
[22] | GIRSHICK R , . Fast R-CNN[C]// Proceedings of IEEE International Conference on Computer Vision. Washington:IEEE Computer Society Press, 2015: 1440-1448. |
[23] | YING Z , LI B , LU H ,et al. Sample-specific SVM learning for person re-identification[C]// Proceedings of IEEE Conference on Computer Vision & Pattern Recognition. Washington:IEEE Computer Society Press, 2016: 1278-187. |
[24] | 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, 2015,39(6): 1137-1149. |
[25] | HE K , GEORGIA G , PIOTR D ,et al. Mask R-CNN[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018:1. |
[26] | SHELHAMER E , LONG J , DARRELL T . Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017,39(4): 640-651. |
[27] | 阮激扬 . 基于 YOLO 的目标检测算法设计与实现[D]. 北京:北京邮电大学, 2019. |
RUAN J Y . Design and implementation of object detection algorithm based on YOLO[D]. Bejing:Beijing University of Posts and Telecommunications, 2019. | |
[28] | REDMON J , DIVVALA S , GIRSHICK R ,et al. You only look once:unified,real-time object detection[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Washington:IEEE Computer Society Press, 2016: 429-442. |
[29] | REDMON J , FARAFADI A . YOLO9000:better,faster,stronger[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recongnition. Piscataway:IEEE Press, 2017: 6517-6525. |
[30] | IOFFE S , SZEGEDY C . Batch normalization:accelerating deep network training by reducing internal covariate shift[C]// Proceedings of International Conference on Machine Learning.[S.l.:s.n]. 2015: 448-456. |
[31] | BOUSMALIS K , TRIGEORGIS G , SILBERMAN N ,et al. Domain separation networks[J]. arXiv:1608.06019, 2016 |
[32] | REDMON J , FARHADI A . YOLOv3:an incremental improvement[J]. arXiv:1608.06019, 2018 |
[33] | LIU W , ANGUELOV D , ERHAN D ,et al. SSD:single shot multibox detector[C]// Proceedings of Computer Vision-ECCV. Springer:International Publishing, 2016: 21-37. |
[34] | FU C Y , LIU W , RANGA A ,et al. DSSD:deconvolutional single shot detector[J]. arXiv:1701.06659, 2017 |
[35] | JISOO J , HYOJIN P , NOJUN K . Enhancement of SSD by concatenating feature maps for object detection[J]. arXiv:1705.09587, 2017 |
[36] | LI Z , ZHOU F Q . FSSD:feature fusion single shot multibox detector[J]. arXiv:1512.02325, 2017 |
[37] | YI J , WU P , METAXAS D N . ASSD:attentive single shot multibox detector[J]. Computer Vision and Image Understanding,arXiv:1909.12456, 2019 |
[38] | HU J , SHEN L , ALBANIE S ,et al. Squeeze-and-excitation networks[C]// Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2018: 7132-7141. |
[39] | LIN T Y , GOYAL P , GIRSHICK R ,et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis &Machine Intelligence, 2017(99): 2999-3007. |
[40] | ERHAN D , SZEGEDY C , TOSHEV A ,et al. Scalable object detection using deep neural networks[J]. arXiv:1312.2249, 2013 |
[41] | LIN T Y , PIOTR D , GIRSHICK R ,et al. Feature pyramid networks for object detection[J]. arXiv:1612.03144, 2016 |
[42] | LAW H , DENG J . CornerNet:detecting objects as paired keypoints[J]. International Journal of Computer Vision, 2018: 734-750. |
[43] | NEWELL A , HUANG Z , DENG J ,et al. Associative embedding:end-to-end learning for joint detection and grouping[C]// Proceedings of Neural Information Processing Systems. Cambridge:MIT Press, 2017: 2277-2287. |
[44] | 唐心宇, 宋爱国 . 人体姿态估计及在康复训练情景交互中的应用[J]. 仪器仪表学报, 2018,39(11): 198-206. |
TANG X Y , SONG A G . Human pose estimation and its implementation in scenario interaction system of rehabilitation training[J]. Chinese Journal of Scientific Instrument, 2018,39(11): 198-206. | |
[45] | GATTUPALLI S , . Human motion analysis and vision-based articulated pose estimation[C]// Proceedings of International Conference on Healthcare Informatics. Piscataway:IEEE Press, 2015: 470-470. |
[46] | HUANG Z , LIU Y , FANG Y ,et al. Video-based fall detection for seniors with human pose estimation[C]// Proceedings of 4th IEEE International Conference on Universal Village 2018. Piscataway:IEEE Press, 2018: 1-4. |
[47] | ZHOU X Y , ZHOU J C , KRHENBUHL P . Bottom-up object detection by grouping extreme and center points[C]// Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2019: 850-859. |
[48] | CAO Z , SIMON T , WEI S ,et al. Realtime multi person 2d pose estimation using part affinity fields[J]. arXiv:1611.08050, 2017 |
[49] | CHEN Y L , WANG Z C , PENG Y X ,et al. Cascaded pyramid network for multi-person pose estimation[C]// Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2018: 7103-7112. |
[50] | XIAO B , WU H P , WEI Y C . Simple baselines for human pose estimation and tracking[J]. arXiv:1804.06208, 2018 |
[51] | DUAN K , BAI S , XIE L ,et al. CenterNet:keypoint triplets for object detection[C]// Proceedings of International Conference on Computer Vision. Piscataway:IEEE Press, 2019: 6568-6577. |
[52] | ZHOU X Y , WANG D Q , KRHENBUHL P . Objects as points[J]. arXiv:1904.07850, 2019 |
[53] | ZHU C C , HE Y H , SAVVIDES M . Feature selective anchor-free module for single-shot object detection[C]// Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2019: 840-849. |
[54] | TIAN Z , SHEN C H , CHEN H ,et al. FCOS:fully convolutional one-stage object detection[C]// Proceedings of IEEE/CVF International Conference on Computer Vision. Piscataway:IEEE Press, 2019: 9626-9635. |
[55] | HE T , SHEN C H , TIAN Z ,et al. Knowledge adaptation for efficient semantic segmentation[J]. arXiv:1903.04688, 2019 |
[56] | LIU Y F , CHEN K , LIU C ,et al. Structured knowledge distillation for semantic segmentation[C]// Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2019: 2599-2608. |
[57] | LONG J , SHELHAMER E , DARRELL T . Fully convolutional networks for semantic segmentation[C]// Proceedings of IEEE Conference on Computer Vision & Pattern Recognition. Piscataway:IEEE Press, 2015. |
[58] | TIAN Z , HE T , SHEN C H ,et al. Decoders matter for semantic segmentation:data-dependent decoding enables flexible feature aggregation[J]. arXiv:1903.02120, 2019 |
[59] | KONG T , SUN F C , LIU H P ,et al. FoveaBox:beyond anchor-based object detector[J]. arXiv:1904.03797, 2019 |
[60] | 邢惠钧, 昌硕 . 基于移动小车的行人监控系统[J]. 电信科学, 2017,33(2): 120-127. |
XING H J , CHANG S . Pedestrian surveillance system based on mobile vehicle[J]. Telecommunications Science, 2017,33(2): 120-127. | |
[61] | 杨恩泽 . 基于深度学习的交通车辆检测与识别算法研究[D]. 北京:北京交通大学, 2019. |
YANG E Z . Vehicle detection and recognition in traffic scenes based on deep learning[D]. Beijing:Beijing Jiaotong University, 2019. | |
[62] | 王忠玉 . 智能视频监控下的安全帽佩戴检测系统的设计与实现[D]. 北京:北京邮电大学, 2018. |
WANG Z Y . Design and implementation of detection system of wearing helmets based on intelligent video surveillance[D]. Beijing:Beijing University of Posts and Telecommunications, 2018. | |
[63] | 陈虹, 郭露露, 宫洵 ,等. 智能时代的汽车控制[J]. 自动化学报, 2019,45(x): 1-21. |
CHEN H , GUO L L , GONG X ,et al. Automotive control in intelligent era[J]. Acta Automatica Sinica, 2019,45(x): 1-21. | |
[64] | RUSSELL B C , TORRALBA A , MURPHY K P ,et al. LabelMe:a database and Web-based tool for image annotation[J]. International Journal of Computer Vision, 2008,77(1): 157-173. |
[65] | HIDAYATULLAH P , MENGKO T E R , MUNIR R ,et al. A semiautomatic sperm cell data annotator for convolutional neural network[C]// Proceedings of 5th International Conference on Science in Information Technology.[S.l.:s.n. ], 2019: 211-216. |
[66] | YU J , MA Z H , WU D ,et al. The safety state control of hazardous chemicals based on multi-source heterogeneous data fusion[C]// Proceedings of 7th International Conference on Computer Science and Network Technology. Piscataway:IEEE Press, 2019: 156-159. |
[67] | LIU S , LIU Y , ZHU X ,et al. Multi-source feature fusion and entropy feature lightweight neural network for constrained multi-state heterogeneous iris recognition[J]. IEEE Access, 2020:1. |
[68] | CHEN K , LI J , LIN W ,et al. Towards accurate one-stage object detection with AP-loss[C]// Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2019: 5114-5122. |
[69] | LIN C H , WANG S , XU D ,et al. Object instance mining for weakly supervised object detection[C]// Proceedings of 34th AAAI Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2020. |
[70] | WANG X , LIU S F , MA H M ,et al. Weakly-supervised semantic segmentation by iterative affinity learning[J]. International Journal of Computer Vision, 2020: 1-14. |
[71] | GAO M , SHEN Y J , LI Q Q ,et al. Residual knowledge distillation[J]. arXiv:2002.09168, 2020 |
[72] | YANG J , MARTINEZ B , BULAT A ,et al. Knowledge distillation via adaptive instance normalization[J]. arXiv:2003.04289, 2020 |
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