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摘要: 近年来, 基于卷积神经网络的目标检测研究发展十分迅速, 各种检测模型的改进方法层出不穷. 本文主要对近几年内目标检测领域中一些具有借鉴价值的研究工作进行了整理归纳. 首先, 对基于卷积神经网络的主要目标检测框架进行了梳理和对比. 其次, 对目标检测框架中主干网络、颈部连接层、锚点等子模块的设计优化方法进行归纳, 给出了各个模块设计优化的基本原则和思路. 接着, 在COCO数据集上对各类目标检测模型进行测试对比, 并根据测试结果分析总结了不同子模块对模型检测性能的影响. 最后, 对目标检测领域未来的研究方向进行了展望.Abstract: In recent years, research on object detection based on convolutional neural network has developed rapidly, and various improved algorithms and models have emerged one after another. This paper mainly summarizes some recent valuable research work in the field of object detection. Firstly, main object detection framework based on convolutional neural network is analyzed and compared. Secondly, the optimization methods of backbone, neck, anchors and other sub-modules are summarized, and the basic principles and ideas for the design and optimization of each modules are given. Thirdly, various objection detection models are tested and compared on the COCO dataset, effects of different sub-modules on the detector performance were analyzed according to the test results. Finally, future research direction in object detection is prospected.
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Key words:
- Convolutional neural network /
- object detection /
- sub-module optimization
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图 3 典型目标检测算法速度−准确率对比
Fig. 3 Speed-accuracy comparison of typical object detection algorithms
图 31 基于注意力机制RPN与多关系头部的少样本检测
Fig. 31 Attention-RPN and multi-relation head based few-shot detection
表 1 各检测模型的性能对比
Table 1 Performance comparison of different object detection models
模型 主干网络 AP AP50 AP75 APS APM APL Faster R-CNN VGG-16 21.9 42.7 — — — — Faster R-CNN R-101* 29.1 48.4 30.7 12.9 35.5 50.9 Faster R-CNN R-101-CBAM 30.8 50.5 32.6 — — — Faster R-CNN++ R-101-FPN 36.2 59.1 39.0 18.2 39.0 48.2 Faster R-CNN++ HR-W32 39.5 61.0 43.1 23.6 42.9 51.0 Faster-DCR V2 R-101 34.3 57.7 35.8 13.8 36.7 51.1 OHEM VGG-16 22.6 42.5 22.2 5.0 23.7 37.9 SIN VGG-16 23.2 44.5 22.0 7.3 24.5 36.3 ION VGG-16 23.6 43.2 22.6 6.4 24.1 38.3 Mask R-CNN R-101-FPN 38.2 60.3 41.7 20.1 41.1 50.2 Mask R-CNN HR-32 40.7 61.8 44.7 25.2 44.4 51.8 Mask R-CNN R-101-FPN+GC 40.8 62.1 45.5 24.4 43.7 51.9 SN-Mask R-CNN R-101-FPN 40.4 58.7 42.5 — — — IN-Mask R-CNN R-101-FPN 40.6 59.4 43.6 24.3 43.9 52.6 R-FCN R-101 29.9 51.9 — 10.8 32.8 45.0 CoupleNet R-101 34.4 54.8 37.2 13.4 38.1 50.8 Cascade R-CNN R-101 42.8 62.1 46.3 23.7 45.5 55.2 Libra R-CNN R-101-FPN 41.1 62.1 44.7 23.4 43.7 52.5 Grid R-CNN[100] X-101* 43.2 63.0 46.6 25.1 46.5 55.2 Light-Head R-CNN R-101 38.2 60.9 41.0 20.9 42.2 52.8 M2Det800 VGG-16 41.0 59.7 45.0 22.1 46.5 53.8 SSD512 VGG-16 28.8 48.5 30.3 10.9 31.8 43.5 GHM SSD X-101 41.6 62.8 44.2 22.3 45.1 55.3 YOLOV3 D-53* 33.0 57.9 34.4 18.3 35.4 41.9 YOLOV3 D-53 34.3 — 36.2 — — — RetinaNet X-101-FPN 39.0 59.4 41.7 22.6 43.4 50.9 GA-RetinaNet X-101-FPN 40.3 60.9 43.5 23.5 44.9 53.5 RefineDet512++ R-101-FPN 41.8 62.9 45.7 25.6 45.1 55.3 FCOS X-101-FPN 42.1 62.1 45.2 25.6 44.9 52.0 FoveaBox X-101-FPN 42.1 61.9 45.2 24.9 46.8 55.6 FSFA X-101-FPN 42.9 63.8 46.3 26.6 46.2 52.7 CornerNet HG-104* 40.5 56.5 43.1 19.4 42.7 53.9 ExtremeNet HG-104 40.2 55.5 43.2 20.4 43.2 53.1 CenterNet HG-104 42.1 61.1 45.9 24.1 45.5 52.8 RepPoints R-101 41.0 62.9 44.3 23.6 44.1 51.7 SNIP++ R-101 43.1 65.3 48.1 26.1 45.9 55.2 SNIPER++ R-101 46.1 67.0 51.6 29.6 48.9 58.1 TridentNet R-101 42.7 63.6 46.5 23.9 46.6 56.6 *注: R-ResNet, X-ResNeXt, HR-HRNet, D-DarkNet, HG-Hourglass. ++表示使用了多尺度、水平翻转等策略 
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表 2 部分检测模型的速度、显存消耗、参数量与计算量对比(基于Titan Xp)
Table 2 Speed, VRAM consumption, parameters and computation comparison of some object detection models (on Titan Xp)
模型 主干网络 训练速度 (s/iter) 显存消耗 (GB) 推理速度 (fps) 参数量 运算次数 Faster R-CNN++ R-101-FPN 0.465 5.7 11.9 60.52×106 283.14×109 Faster R-CNN++ HR-W32 0.593 5.9 8.5 45.0×106 245.3×109 Mask R-CNN R-101-FPN 0.571 5.8 9.4 62.81×106 351.65×109 Mask R-CNN x-101-FPN 0.759 7.1 8.3 63.17×106 355.4×109 Mask R-CNN R-101-FPN+GC 0.731 7.0 8.6 82.13×106 352.8×109 R-FCN R-101 0.400 5.6 14.6 — — Cascade R-CNN R-101-FPN 0.584 6.0 10.3 87.8×106 310.78×109 Cascade R-CNN X-101-FPN 0.770 8.4 8.9 88.16×106 314.53×109 Libra R-CNN R-101-FPN 0.495 6.0 10.4 60.79×106 284.19×109 Grid R-CNN X-101-FPN 1.214 6.7 10.0 82.95×106 409.19×109 M2Det800 VGG-16 — — 11.8 — — SSD512 VGG-16 0.412 7.6 20.7 36.04×106 386.02×109 GHM RetinaNet X-101-FPN 0.818 7.0 7.6 56.74×106 319.14×109 RetinaNet X-101-FPN 0.632 6.7 9.3 56.37×106 319.04×109 GA-RetinaNet X-101-FPN 0.870 6.7 7.5 56.01×106 283.13×109 FCOS R-101-FPN 0.558 9.4 11.6 50.96×106 276.53×109 CornerNet HG$-104^*$ — — 4.9 — — ExtremeNet HG-104 — — 3.1 — — CenterNet HG-104 — 11.91 8.5 — — RepPoints R-101 0.558 5.6 10.9 55.62×106 266.23×109 SNIP++ R-101 — — < 1.0 — — SNIPER++ R-101 — — 4.8 — — TridentNet R-101 0.985 6.6 2.1 — — *注: R-ResNet, X-ResNeXt, HR-HRNet, D-DarkNet, HG-Hourglass. ++表示使用了多尺度、水平翻转等策略
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